{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T14:22:54Z","timestamp":1762957374450,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T00:00:00Z","timestamp":1709078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Cosimo Distante"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>COVID-19 analysis from medical imaging is an important task that has been intensively studied in the last years due to the spread of the COVID-19 pandemic. In fact, medical imaging has often been used as a complementary or main tool to recognize the infected persons. On the other hand, medical imaging has the ability to provide more details about COVID-19 infection, including its severity and spread, which makes it possible to evaluate the infection and follow-up the patient\u2019s state. CT scans are the most informative tool for COVID-19 infection, where the evaluation of COVID-19 infection is usually performed through infection segmentation. However, segmentation is a tedious task that requires much effort and time from expert radiologists. To deal with this limitation, an efficient framework for estimating COVID-19 infection as a regression task is proposed. The goal of the Per-COVID-19 challenge is to test the efficiency of modern deep learning methods on COVID-19 infection percentage estimation (CIPE) from CT scans. Participants had to develop an efficient deep learning approach that can learn from noisy data. In addition, participants had to cope with many challenges, including those related to COVID-19 infection complexity and crossdataset scenarios. This paper provides an overview of the COVID-19 infection percentage estimation challenge (Per-COVID-19) held at MIA-COVID-2022. Details of the competition data, challenges, and evaluation metrics are presented. The best performing approaches and their results are described and discussed.<\/jats:p>","DOI":"10.3390\/s24051557","type":"journal-article","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T09:26:17Z","timestamp":1709112377000},"page":"1557","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5077-4862","authenticated-orcid":false,"given":"Fares","family":"Bougourzi","sequence":"first","affiliation":[{"name":"Institute of Applied Sciences and Intelligent Systems, National Research Council of Italy, 73100 Lecce, Italy"},{"name":"Laboratoire LISSI, University Paris-Est Creteil, Vitry sur Seine, 94400 Paris, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1073-2390","authenticated-orcid":false,"given":"Cosimo","family":"Distante","sequence":"additional","affiliation":[{"name":"Institute of Applied Sciences and Intelligent Systems, National Research Council of Italy, 73100 Lecce, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6581-9680","authenticated-orcid":false,"given":"Fadi","family":"Dornaika","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV\/EHU, Manuel Lardizabal, 1, 20018 San Sebastian, Spain"},{"name":"IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8750-1905","authenticated-orcid":false,"given":"Abdelmalik","family":"Taleb-Ahmed","sequence":"additional","affiliation":[{"name":"Institut d\u2019Electronique de Micro\u00e9lectronique et de Nanotechnologie (IEMN), UMR 8520, Universite Polytechnique Hauts-de-France, Universit\u00e9 de Lille, CNRS, 59313 Valenciennes, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9092-735X","authenticated-orcid":false,"given":"Abdenour","family":"Hadid","sequence":"additional","affiliation":[{"name":"Sorbonne Center for Artificial Intelligence, Sorbonne University of Abu Dhabi, Abu Dhabi P.O. 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