COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge
Fares Bougourzi,
Cosimo Distante,
Fadi Dornaika,
Abdelmalik Taleb-Ahmed,
Abdenour Hadid,
Suman Chaudhary,
Wanting Yang,
Yan Qiang,
Talha Anwar,
Mihaela Elena Breaban,
Chih-Chung Hsu,
Shen-Chieh Tai,
Shao-Ning Chen,
Davide Tricarico,
Hafiza Ayesha Hoor Chaudhry,
Attilio Fiandrotti,
Marco Grangetto,
Maria Ausilia Napoli Spatafora,
Alessandro Ortis,
Sebastiano Battiato
Affiliations
Fares Bougourzi
Institute of Applied Sciences and Intelligent Systems, National Research Council of Italy, 73100 Lecce, Italy
Cosimo Distante
Institute of Applied Sciences and Intelligent Systems, National Research Council of Italy, 73100 Lecce, Italy
Fadi Dornaika
Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU, Manuel Lardizabal, 1, 20018 San Sebastian, Spain
Abdelmalik Taleb-Ahmed
Institut d’Electronique de Microélectronique et de Nanotechnologie (IEMN), UMR 8520, Universite Polytechnique Hauts-de-France, Université de Lille, CNRS, 59313 Valenciennes, France
Abdenour Hadid
Sorbonne Center for Artificial Intelligence, Sorbonne University of Abu Dhabi, Abu Dhabi P.O. Box 38044, United Arab Emirates
Suman Chaudhary
College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China
Wanting Yang
College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China
Yan Qiang
College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China
Talha Anwar
School of Computing, National University of Computer and Emerging Sciences, Islamabad 44000, Pakistan
Mihaela Elena Breaban
Faculty of Computer Science, Alexandru Ioan Cuza University, 700506 Iasi, Romania
Chih-Chung Hsu
Institute of Data Science, National Cheng Kung University, No. 1, University Rd., East Dist., Tainan City 701, Taiwan
Shen-Chieh Tai
Institute of Data Science, National Cheng Kung University, No. 1, University Rd., East Dist., Tainan City 701, Taiwan
Shao-Ning Chen
Institute of Data Science, National Cheng Kung University, No. 1, University Rd., East Dist., Tainan City 701, Taiwan
Davide Tricarico
Dipartimento di Informatica, Universita degli Studi di Torino, Corso Svizzera 185, 10149 Torino, Italy
Hafiza Ayesha Hoor Chaudhry
Dipartimento di Informatica, Universita degli Studi di Torino, Corso Svizzera 185, 10149 Torino, Italy
Attilio Fiandrotti
Dipartimento di Informatica, Universita degli Studi di Torino, Corso Svizzera 185, 10149 Torino, Italy
Marco Grangetto
Dipartimento di Informatica, Universita degli Studi di Torino, Corso Svizzera 185, 10149 Torino, Italy
Maria Ausilia Napoli Spatafora
Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy
Alessandro Ortis
Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy
Sebastiano Battiato
Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy
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’s 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.
