{"id":829,"date":"2017-08-29T17:53:29","date_gmt":"2017-08-29T17:53:29","guid":{"rendered":"https:\/\/fei-lab.org\/?p=829"},"modified":"2023-12-28T20:01:26","modified_gmt":"2023-12-28T20:01:26","slug":"hyperspectral-imaging","status":"publish","type":"post","link":"https:\/\/fei-lab.org\/hyperspectral-imaging\/","title":{"rendered":"Hyperspectral Imaging"},"content":{"rendered":"\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-av_heading-3aae5547005b39a22186f1ef50e0e04e\">\n#top .av-special-heading.av-av_heading-3aae5547005b39a22186f1ef50e0e04e{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-av_heading-3aae5547005b39a22186f1ef50e0e04e .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-av_heading-3aae5547005b39a22186f1ef50e0e04e .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-av_heading-3aae5547005b39a22186f1ef50e0e04e av-special-heading-h3  avia-builder-el-0  el_before_av_video  avia-builder-el-first '><h3 class='av-special-heading-tag '  itemprop=\"headline\"  >Hyperspectral Imaging (HSI)<\/h3><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div>\n<div  class='avia-video av-ko4e2137-c7aec6fee05861649f3da789db92184f avia-video-16-9 av-no-preview-image avia-video-load-always av-lazyload-immediate av-lazyload-video-embed'  itemprop=\"video\" itemtype=\"https:\/\/schema.org\/VideoObject\"  data-original_url='https:\/\/www.youtube.com\/watch?v=xbMAa-MzNXk'><script type='text\/html' class='av-video-tmpl'><div class='avia-iframe-wrap'><iframe loading=\"lazy\" title=\"Medical Hyperspectral Imaging: Artificial Intelligence and Image-Guided Surgery\" width=\"1500\" height=\"844\" src=\"https:\/\/www.youtube.com\/embed\/xbMAa-MzNXk?feature=oembed&autoplay=0&loop=0&controls=1&mute=0\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen><\/iframe><\/div><\/script><div class='av-click-to-play-overlay'><div class=\"avia_playpause_icon\"><\/div><\/div><\/div>\n<section  class='av_textblock_section av-9dw7x-3068813710cb8113c87d6145e96e70de '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p align=\"justify\">Hyperspectral imaging (HSI) also called imaging spectrometer is an emerging imaging modality for medical applications. HSI has been explored for various remote sensing applications by NASA. With the advantages of acquiring two dimensional images across a wide range of electromagnetic spectrum, HSI has been applied to archaeology and art conservation, vegetation and water resource control, food quality and safety control, forensic medicine, crime scene detection, and biomedical areas, etc.<\/p>\n<p align=\"justify\">HSI offers great potential for non-invasive disease diagnosis and surgical guidance. Light delivered to the biological tissue undergoes multiple scattering from inhomogeneity of biological structures and absorption primarily in hemoglobin, melanin and water as it propagates through tissue. It is assumed that the absorption, fluorescence and scattering characteristics of tissue change during the progression of disease, therefore the reflected, fluorescent and transmitted light from tissue captured by HSI carries quantitative diagnostic information about tissue pathology. In recent years, the advancements of hyperspectral cameras, image analysis methods and computational power make it possible for many exciting applications in the medical field.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-59\" src=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/HSI_pushbroom.png\" alt=\"\" width=\"1431\" height=\"494\" srcset=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/HSI_pushbroom.png 1431w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/HSI_pushbroom-300x104.png 300w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/HSI_pushbroom-768x265.png 768w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/HSI_pushbroom-1030x356.png 1030w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/HSI_pushbroom-705x243.png 705w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/HSI_pushbroom-450x155.png 450w\" sizes=\"auto, (max-width: 1431px) 100vw, 1431px\" \/><\/p>\n<p>Schematic diagram of a pushbroom hyperspectral imaging system<\/p>\n<div id=\"attachment_60\" style=\"width: 1441px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-60\" class=\"size-full wp-image-60\" src=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/HSI_RGB_Hypercube.png\" alt=\"\" width=\"1431\" height=\"423\" srcset=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/HSI_RGB_Hypercube.png 1431w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/HSI_RGB_Hypercube-300x89.png 300w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/HSI_RGB_Hypercube-768x227.png 768w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/HSI_RGB_Hypercube-1030x304.png 1030w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/HSI_RGB_Hypercube-705x208.png 705w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/HSI_RGB_Hypercube-450x133.png 450w\" sizes=\"auto, (max-width: 1431px) 100vw, 1431px\" \/><p id=\"caption-attachment-60\" class=\"wp-caption-text\">Comparison between hypercube and RGB image. Hypercube is three dimensional dataset a 2D image on each wavelength. The lower left is the reflectance curve (spectral signature) of a pixel in the image. RGB color image only has three image bands on red, green and blue wavelength respectively. The lower right is the intensity curve of a pixel in the RGB image.<\/p><\/div>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-61\" src=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/HSI_DataStructure.png\" alt=\"\" width=\"1430\" height=\"649\" srcset=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/HSI_DataStructure.png 1430w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/HSI_DataStructure-300x136.png 300w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/HSI_DataStructure-768x349.png 768w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/HSI_DataStructure-1030x467.png 1030w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/HSI_DataStructure-705x320.png 705w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/HSI_DataStructure-450x204.png 450w\" sizes=\"auto, (max-width: 1430px) 100vw, 1430px\" \/><\/p>\n<\/div><\/section>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-av_heading-eb7875798dbaca04e58bd89c1da8f490\">\n#top .av-special-heading.av-av_heading-eb7875798dbaca04e58bd89c1da8f490{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-av_heading-eb7875798dbaca04e58bd89c1da8f490 .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-av_heading-eb7875798dbaca04e58bd89c1da8f490 .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-av_heading-eb7875798dbaca04e58bd89c1da8f490 av-special-heading-h4  avia-builder-el-3  el_after_av_textblock  el_before_av_textblock '><h4 class='av-special-heading-tag '  itemprop=\"headline\"  >Selected Publications<\/h4><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div>\n<section  class='av_textblock_section av-k5pei4wd-a8c2b50b51c6f1f6d88bceb946021181 '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" >\r\n<style>\r\n\tp {display: inline;}\r\n<\/style>\r\n<ul>\r\n\r\n<li>\r\n\r\n<p>Ma L, Rathgeb A, Mubarak H, Tran M, <strong>Fei B<\/strong>. Unsupervised super-resolution reconstruction of hyperspectral histology images for whole-slide imaging. Journal of biomedical optics. 2022 May 1;27(5):056502-.<\/p>\n \r\n\r\n\r\n\r\n\r\nMa_2022_JBO_SuperRes_Reconstruction_WSI\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/35578386\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2023\/02\/Ma_2022_JBO_SuperRes_Reconstruction_WSI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/1.JBO.27.5.059801\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2023\/02\/Ma_2022_JBO_SuperRes_Reconstruction_WSI_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2023\/02\/Ma_2022_JBO_SuperRes_Reconstruction_WSI_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Tran MH, Ma L, Litter JV, Chen AY, <strong>Fei B<\/strong>. Thyroid carcinoma detection on whole histologic slides using hyperspectral imaging and deep learning. In Medical Imaging 2022: Digital and Computational Pathology 2022 Apr 4 (Vol. 12039, pp. 101-111). SPIE.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_SPIE_Minh_HSI_Histology\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/36798939\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Minh_HSI_Histology.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2612963\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Minh_HSI_Histology_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Minh_HSI_Histology_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Ma L, Rathgeb A, Tran M, <strong>Fei B<\/strong>. Unsupervised super resolution network for hyperspectral histologic imaging. In Medical Imaging 2022: Digital and Computational Pathology 2022 Apr 4 (Vol. 12039, pp. 149-159). SPIE.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_SPIE_Ling_HSI_Super_Resolution\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/36793770\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Ling_HSI_Super_Resolution.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2611889\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Ling_HSI_Super_Resolution_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Ling_HSI_Super_Resolution_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Leitch K, Halicek M, Shahedi M, Little JV, Chen AY, <strong>Fei B<\/strong>. Detecting aggressive papillary thyroid carcinoma using hyperspectral imaging and radiomic features. In Medical Imaging 2022: Computer-Aided Diagnosis 2022 Apr 4 (Vol. 12033, pp. 537-544). SPIE.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_SPIE_KaToria_HSI_Thyroid_Radiomics\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/30220773\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_KaToria_HSI_Thyroid_Radiomics.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2611842\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_KaToria_HSI_Thyroid_Radiomics_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_KaToria_HSI_Thyroid_Radiomics_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Modir N, Shahedi M, Dormer J, Ma L, Ghaderi M, Sirsi S, Cheng YS, <strong>Fei B<\/strong>. LED-based hyperspectral endoscopic imaging. In Optical Biopsy XX: Toward Real-Time Spectroscopic Imaging and Diagnosis 2022 Mar 2 (Vol. 11954, pp. 47-58). SPIE.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_Photoncis_Naeeme_LED_Endoscope\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/36794092\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_Photoncis_Naeeme_LED_Endoscope.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2609023\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_Photoncis_Naeeme_LED_Endoscope_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_Photoncis_Naeeme_LED_Endoscope_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Ayala L, Isensee F, Wirkert SJ, Vemuri AS, Maier-Hein KH, <strong>Fei B<\/strong>, Maier-Hein L. Band selection for oxygenation estimation with multispectral\/hyperspectral imaging. Biomedical Optics Express. 2022 Mar 1;13(3):1224-42.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_BOE_Band_Selection\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/35414995\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_BOE_Band_Selection.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1364\/BOE.441214\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_BOE_Band_Selection_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_BOE_Band_Selection_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Zhou X, Ma L, Brown W, Little JV, Chen AY, Myers LL, Sumer BD, <strong>Fei B<\/strong>. Automatic detection of head and neck squamous cell carcinoma on pathologic slides using polarized hyperspectral imaging and machine learning. In Medical Imaging 2021: Digital Pathology 2021 Feb 15 (Vol. 11603, p. 116030Q). International Society for Optics and Photonics.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2021_Zhou_HSI_HNC\r\n\r\n\t\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Zhou_HSI_HNC.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Zhou_HSI_HNC_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Zhou_HSI_HNC_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Ma L, Shahedi M, Shi T, Halicek M, Little JV, Chen AY, Myers LL, Sumer BD, <strong>Fei B<\/strong>. Pixel-level tumor margin assessment of surgical specimen in hyperspectral imaging and deep learning classification. In Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling 2021 Feb 15 (Vol. 11598, p. 1159811). International Society for Optics and Photonics.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2021_Ma_Margin_HSI_Deep_Learning\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/35755403\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Ma_Margin_HSI_Deep_Learning.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2581046\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Ma_Margin_HSI_Deep_Learning_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Ma_Margin_HSI_Deep_Learning_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Ma L, Zhou X, Little JV, Chen AY, Myers LL, Sumer BD, <strong>Fei B<\/strong>. Hyperspectral microscopic imaging for the detection of head and neck squamous cell carcinoma on histologic slides. In Medical Imaging 2021: Digital Pathology 2021 Feb 15 (Vol. 11603, p. 116030P). International Society for Optics and Photonics.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2021_Ma_HSI_Histology_HNC\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/35783088\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2021\/02\/Fei_2021_Ma_HSI_Histology_HNC.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2581970\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2021\/02\/Fei_2021_Ma_HSI_Histology_HNC_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2021\/02\/Fei_2021_Ma_HSI_Histology_HNC_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Edwards K, Halicek M, Little JV, Chen AY, <strong>Fei B<\/strong>. Multiparametric radiomics for predicting the aggressiveness of papillary thyroid carcinoma using hyperspectral images. In Medical Imaging 2021: Computer-Aided Diagnosis 2021 Feb 15 (Vol. 11597, p. 1159728). International Society for Optics and Photonics.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2021_Edwards_Radiomics_HSI\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/35756897\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2021\/02\/Fei_2021_Edwards_Radiomics_HSI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2582147\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2021\/02\/Fei_2021_Edwards_Radiomics_HSI_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2021\/02\/Fei_2021_Edwards_Radiomics_HSI_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Ortega S, Halicek M, Fabelo H, Quevedo E, <strong>Fei B<\/strong>, Callico GM. Information Extraction Techniques in Hyperspectral Imaging Biomedical Applications. InMultimedia Information Retrieval 2020 Oct 10. IntechOpen.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2020_HSI_Book_Chapter\r\n\r\n\t\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2020_HSI_Book_Chapter.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2020_HSI_Book_Chapter_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2020_HSI_Book_Chapter_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<div class=\"gs_citr\" tabindex=\"0\">Ortega S, Halicek M, Fabelo H, Camacho R, Plaza MD, Godtliebsen F, M Callic\u00f3 G, <strong>Fei BW <\/strong>(Corresponding author). Hyperspectral imaging for the detection of glioblastoma tumor cells in H&amp;E slides using convolutional neural networks. Sensors; 20(7):1911.<\/div>\n \r\n\r\n\r\n\r\n\r\nOrtega_2020_Sensors\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32235483\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_Samuel_Sensors_GBM_Cells_HSI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.3390\/s20071911\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Ortega_2020_Sensors_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Ortega_2020_Sensors_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<div class=\"gs_citr\" tabindex=\"0\">Zhou X, Ma L, Halicek M, Dormer J, <strong>Fei BW <\/strong>(Corresponding author). Development of a new polarized hyperspectral imaging microscope. Imaging, Therapeutics, and Advanced Technology in Head and Neck Surgery and Otolaryngology 2020; 11213(1121308). International Society for Optics and Photonics,<\/div>\n \r\n\r\n\r\n\r\n\r\nZhou_2020_Otolaryngology\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32577044\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_Ximing_PHSI_1121308.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2549676\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Zhou_2020_Otolaryngology_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Zhou_2020_Otolaryngology_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<div class=\"gs_citr\" tabindex=\"0\">Ma L, Halicek M, <strong>Fei BW<\/strong>. In vivo cancer detection in animal model using hyperspectral image classification with wavelet feature extraction. Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging; 11317(113171C). International Society for Optics and Photonics,<\/div>\n \r\n\r\n\r\n\r\n\r\nMa_2020_SPIE_2\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32476705\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_LingMa_HSI_113171C.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2549397\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Ma_2020_SPIE_2_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Ma_2020_SPIE_2_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<div class=\"gs_citr\" tabindex=\"0\">Halicek M, Dormer JD, Little JV, Chen AY, <strong>Fei BW <\/strong>(Corresponding author). Tumor detection of the thyroid and salivary glands using hyperspectral imaging and deep learning. Biomedical Optics Express; 11(3):1383-400.<\/div>\n \r\n\r\n\r\n\r\n\r\nHalicek_2020_BOE_2\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32206417\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_BOE_Halicek_Tumor_detection_HSI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1364\/BOE.381257\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Halicek_2020_BOE_2_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Halicek_2020_BOE_2_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Ortega S, Halicek M, Fabelo H, Guerra R, Lopez C, Lejeune M, Godtliebsen F, Callico GM, <strong>Fei BW <\/strong>(Corresponding author). Hyperspectral imaging and deep learning for the detection of breast cancer cells in digitized histological images. Medical Imaging 2020: Digital Pathology; 11320(113200V). International Society for Optics and Photonics,<\/p>\n \r\n\r\n\r\n\r\n\r\nOrtega_2020_SPIE\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32528219\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_Orgega_HSI_113200V.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2548609\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Ortega_2020_SPIE_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Ortega_2020_SPIE_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Halicek M, Ortega S, Fabelo H, Lopez C, Lejeune M, Callico GM, <strong>Fei BW <\/strong>(Corresponding author). Conditional generative adversarial network for synthesizing hyperspectral images of breast cancer cells from digitized histology. Medical Imaging 2020: Digital Pathology; 11320(113200U). International Society for Optics and Photonics,<\/p>\n \r\n\r\n\r\n\r\n\r\nHalicek_2020_SPIE\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32528218\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_Martin_HSI_113200U.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2549994\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Halicek_2020_SPIE_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Halicek_2020_SPIE_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Ma L, Halicek M, Zhou X, Dormer J, <strong>Fei BW <\/strong>(Corresponding author). Hyperspectral microscopic imaging for automatic detection of head and neck squamous cell carcinoma using histologic image and machine learning. Medical Imaging 2020: Digital Pathology; 11320(113200W). International Society for Optics and Photonics,<\/p>\n \r\n\r\n\r\n\r\n\r\nMa_2020_SPIE\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32476708\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_MaLing_HSI_113200W.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2549369\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Ma_2020_SPIE_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Ma_2020_SPIE_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Huang J, Halicek M, Shahedi M,\u00a0<strong>Fei BW <\/strong>(Corresponding author). Augmented reality visualization of hyperspectral imaging classifications for image-guided brain tumor resection. Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; 11315(113150U). International Society for Optics and Photonics,<\/p>\n \r\n\r\n\r\n\r\n\r\nHuang_2020_SPIE\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32606488\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_Huang_AR_113150U.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2549041\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Huang_2020_SPIE_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Huang_2020_SPIE_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Ortega S, Halicek M, Fabelo H, Callico GM, <strong>Fei BW<\/strong><strong>\u00a0<\/strong>(Corresponding author). Hyperspectral and multispectral imaging in digital and computational pathology: a systematic review. Biomedical Optics Express; 11(6): 3195-3233.<\/p>\n \r\n\r\n\r\n\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32637250\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_BOE_Halicek_Hyperspectral_systematic_review.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1364\/BOE.386338\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p><strong>Fei BW<\/strong>. Hyperspectral imaging in medical applications. Data Handling in Science and Technology; 32: 523-565. Elsevier,<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2020_DHST\r\n\r\n\t\r\n\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/01\/Fei_2020_DHST_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/01\/Fei_2020_DHST_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Halicek M, Himar F, Ortega S, Little JV, Wang X, Chen AY, Callic\u00f3 GM, Myers LL, Sumer BD, <strong>Fei BW <\/strong>(Corresponding author). Cancer detection using hyperspectral imaging and evaluation of the superficial tumor margin variance with depth. Proceedings of SPIE Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling.<\/p>\n \r\n\r\n\r\n\r\n\r\nHalicek_2019_SPIE_1\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/25426271\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2019_SPIE_Halicek_Cancer_detection_Hyperspectral_Superficial_tumor_margin.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2512985\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Halicek_2019_SPIE_1_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Halicek_2019_SPIE_1_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Fabelo H, Halicek M, Ortega S, Shahedi M, Szolna A, Pineiro JF, Sosa C, O&#8217;Shanahan AJ, Bisshopp S, Espino C, Marquez M, Hernandez M, Carrera D, Morera J, Callic\u00f3 GM, Sarmiento R, <strong>Fei BW<\/strong> (Corresponding author). Deep learning-based framework for in-vivo identification of glioblastoma tumor using hyperspectral images of human brain. Sensors;19(4).<\/p>\n \r\n\r\n\r\n\r\n\r\nFabelo_2019_Sensors\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/30813245\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2019_Sensors_Fabelo_Deep_learning_Framework_Glioblastoma_Hyperspectral_Brain.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.3390\/s19040920\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Fabelo_2019_Sensors_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Fabelo_2019_Sensors_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Halicek M, Little JV, Wang X, Chen AY, <strong>Fei BW<\/strong> (Corresponding author). Optical biopsy of head and neck cancer using hyperspectral imaging and convolution neural networks. Journal of Biomedical Optics;24(3):1-9.<\/p>\n \r\n\r\n\r\n\r\n\r\nHalicek_2019_JBO\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/30197462\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2019_JBO_Halicek_Optical_biopsy_Hyperspectral_CNN.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/1.JBO.24.3.036007\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Halicek_2019_JBO_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Halicek_2019_JBO_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Halicek M, Little JV, Wang X, Chen ZG, Patel M, Griffith CC, El-Diery MW, Sava NF, Chen AY, <strong>Fei BW<\/strong> (Corresponding author). Deformable registration of histological cancer margins to gross hyperspectral images using demons. Proceedings of SPIE: The International Society for Optical Engineering;10581.<\/p>\n \r\n\r\n\r\n\r\n\r\nHalicek_2018_MedImaging_1\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/30220773\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2018_SPIE_Halicek_Deformable_registration_Cancer_margins_Hyperspectral_Demons.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2293165\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Halicek_2018_MedImaging_1_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Halicek_2018_MedImaging_1_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Halicek M, Little JV, Wang X, Patel M, Griffith CC, El-Diery MW, Chen AY, <strong>Fei BW<\/strong> (Corresponding author). Optical biopsy of head and neck cancer using hyperspectral imaging and convolution neural networks. Proceedings of SPIE: The Internation Society for Optical Engineering;10469.<\/p>\n \r\n\r\n\r\n\r\n\r\nHalicek_2018_SPIE\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/30891966\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2018_SPIE_Halicek_Optical_biopsy_Head_Neck_Hyperspectral_CNN.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/1.JBO.24.3.036007\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Halicek_2018_SPIE_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Halicek_2018_SPIE_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Halicek M, Little JV, Wang X, Patel M, Griffith CC, Chen AY, <strong>Fei BW<\/strong> (Corresponding author). Tumor margin classification of head and neck cancer using hyperspectral imaging and convolutional neural networks. Proceedings of SPIE: The International Society for Optical Engineering;10576.<\/p>\n \r\n\r\n\r\n\r\n\r\nHalicek_2018_MedImaging\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/30245540\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2018_SPIE_Halicek_Tumor_margin_Head_Neck_Hyperspectral_CNN.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2293167\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Halicek_2018_MedImaging_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Halicek_2018_MedImaging_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Halicek, M., Lu, GL., Little, JV., Wang, X., Patel, M., Griffith, CC., El-Deiry, MW., Chen, A., <strong>Fei, BW<\/strong>.(2017). \u201cDeep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging.\u201dJournal of Biomedical Optics 22(6): 4.<\/p>\n \r\n\r\n\r\n\r\n\r\nHalicek_2017_JBO\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/28655055\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/Halicek-2017-Deep-convolutional-neural-network.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/1.JBO.22.6.060503\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Halicek_2017_JBO_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Halicek_2017_JBO_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Ma L, Lu G, Wang D, Wang X, Chen ZG, <strong>Fei BW<\/strong>. Deep learning-based classification for head and neck cancer detection with hyperspectral imaging in an animal model, Proc. SPIE 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, 101372G, March 13, 2017, Orlando, FL.<\/p>\n \r\n\r\n\r\n\r\n\r\nMa_2017_MedImaging\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/38486806\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2017_SPIE_Ma_Deep_learning_Head_Neck_Classification_Hyperspectral_Animal_model.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2255562\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Ma_2017_MedImaging_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Ma_2017_MedImaging_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Lu G, Zhang H, Wang X, Little J, Magliocca K, Chen A, <strong>Fei BW<\/strong> (Corresponding author).\u201cDetection of head and neck cancer in surgical specimens using quantitative hyperspectral imaging,\u201d Clinical Cancer Research. 2017 Jun 13. pii: clincanres.0906.2017. doi: 10.1158\/1078-0432.CCR-17-0906. [Epub ahead of print] PubMed PMID:28611203.<\/p>\n \r\n\r\n\r\n\r\n\r\nLu_2017_CCR\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/28611203\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2017_CCR_Lu_Detection_Head_Neck_Surgical_specimens_Hyperspectral.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1158\/1078-0432.CCR-17-0906\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Lu_2017_CCR_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Lu_2017_CCR_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p><strong>Fei BW<\/strong>, Lu G, Wang X, Zhang H, Little JV, Magliocca KR, Chen AY, Tumor margin assessment of surgical tissue specimen of cancer patients using label-free hyperspectral imaging, Proc. SPIE 10054, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XV, 100540E, February 14, 2017, San Francisco, CA.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2017_SPIE\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/30294063\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2017_SPIE_Tumor_margin_Surgical_tissue_Label_free_hyperspectral.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2249803\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Fei_2017_SPIE_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Fei_2017_SPIE_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Lu G, Wang D, Qin X, Halig L, Muller S, Zhang H, Chen A, Pogue BW, Chen ZG, <strong>Fei BW<\/strong> (Corresponding author). Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery. Journal of Biomedical Optics 2015; 20:126012.<\/p>\n \r\n\r\n\r\n\r\n\r\nLu_2015_JBO\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/26720879\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2015_JBO_Lu_Framework_Hyperspectral_Processing_Animal_tumor_surgery.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/1.JBO.20.12.126012\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Lu_2015_JBO_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Lu_2015_JBO_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Lu G, Qin X, Wang D, Chen ZG, <strong>Fei BW<\/strong> (Corresponding author). Quantitative wavelength analysis and image classification for intraoperative cancer diagnosis with hyperspectral imaging. Proceedings of SPIE \u2013 The International Society for Optical Engineering 2015; 9415: 94151B. PubMed PMID:26523083.<\/p>\n \r\n\r\n\r\n\r\n\r\nLu_2015_MedImaging\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/26523083\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2015_SPIE_Lu_Quantitative_wavelength_analysis_HSI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Lu_2015_MedImaging_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Lu_2015_MedImaging_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Pike R, Patton SK, Lu G, Halig LV, Wang D, Chen ZG, <strong>Fei BW<\/strong> (Corresponding author). A minimum spanning forest based hyperspectral image classification method for cancerous tissue detection. Proceedings of SPIE \u2013 The International Society for Optical Engineering 2014; 9034:90341W. PubMed PMID:25426272.<\/p>\n \r\n\r\n\r\n\r\n\r\nPike_2014_MedImaging\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/25426272\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2014_SPIE_Pike_Minimum_spanning_forest_Hyperspectral.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Pike_2014_MedImaging_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Pike_2014_MedImaging_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<\/ul>\r\n\n<\/div><\/section>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":4320,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[28,2],"tags":[],"class_list":["post-829","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-main-research-post","category-research"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Hyperspectral Imaging &#8226; Quantitative Bioimaging Laboratory<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fei-lab.org\/hyperspectral-imaging\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Hyperspectral Imaging &#8226; Quantitative Bioimaging Laboratory\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fei-lab.org\/hyperspectral-imaging\/\" \/>\n<meta property=\"og:site_name\" content=\"Quantitative Bioimaging Laboratory\" \/>\n<meta property=\"article:published_time\" content=\"2017-08-29T17:53:29+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-12-28T20:01:26+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/08\/FI-hyperspectral-300-x-300.png\" \/>\n\t<meta property=\"og:image:width\" content=\"300\" \/>\n\t<meta property=\"og:image:height\" content=\"300\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"awp-admin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"awp-admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/fei-lab.org\\\/hyperspectral-imaging\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/fei-lab.org\\\/hyperspectral-imaging\\\/\"},\"author\":{\"name\":\"awp-admin\",\"@id\":\"https:\\\/\\\/fei-lab.org\\\/#\\\/schema\\\/person\\\/1a52ce2f3ee13559a03a32ac20076dc0\"},\"headline\":\"Hyperspectral Imaging\",\"datePublished\":\"2017-08-29T17:53:29+00:00\",\"dateModified\":\"2023-12-28T20:01:26+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/fei-lab.org\\\/hyperspectral-imaging\\\/\"},\"wordCount\":415,\"publisher\":{\"@id\":\"https:\\\/\\\/fei-lab.org\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/fei-lab.org\\\/hyperspectral-imaging\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/fei-lab.org\\\/wp-content\\\/uploads\\\/2017\\\/08\\\/FI-hyperspectral-300-x-300.png\",\"articleSection\":[\"Research Areas\",\"Research Topics\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/fei-lab.org\\\/hyperspectral-imaging\\\/\",\"url\":\"https:\\\/\\\/fei-lab.org\\\/hyperspectral-imaging\\\/\",\"name\":\"Hyperspectral Imaging &#8226; 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