1. Can AI predict epithelial lesion categories via automated analysis of cervical biopsies: The TissueNet challenge?
- Author
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Nicolas Loménie, Capucine Bertrand, Rutger H.J. Fick, Saima Ben Hadj, Brice Tayart, Cyprien Tilmant, Isabelle Farré, Soufiane Z. Azdad, Samy Dahmani, Gilles Dequen, Ming Feng, Kele Xu, Zimu Li, Sophie Prevot, Christine Bergeron, Guillaume Bataillon, Mojgan Devouassoux-Shisheboran, Claire Glaser, Agathe Delaune, Séverine Valmary-Degano, Philippe Bertheau, Laboratoire d'Informatique Paris Descartes (LIPADE (URP_2517)), Université Paris Cité (UPCité), Morphologie et Images (MORPHEME), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut de Biologie Valrose (IBV), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Signal, Images et Systèmes (Laboratoire I3S - SIS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Modélisation, Information et Systèmes - UR UPJV 4290 (MIS), Université de Picardie Jules Verne (UPJV), CSIRO Indian Ocean Marine Research Centre [Australia], The University of Western Australia (UWA), Centre for Southern Hemisphere Oceans Research, Hobart, Tasmania, Australia, Institut Langevin - Ondes et Images (UMR7587) (IL), Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Yangtze Delta Region Institute of Tsinghua University [Zhejiang], AP-HP Hôpital Bicêtre (Le Kremlin-Bicêtre), Laboratoire CERBA [Saint Ouen l'Aumône], Institut Curie [Paris], Hospices Civils de Lyon (HCL), Université de Lyon - UDL, GenoSplice [Paris], Service d'Anatomie pathologique [CHRU Besançon], and Centre Hospitalier Régional Universitaire de Besançon (CHRU Besançon)
- Subjects
Artificial intelligence ,[SDV]Life Sciences [q-bio] ,Data challenge ,Uterine cervix ,Whole slide images ,Health Informatics ,Computer Science Applications ,Pathology and Forensic Medicine ,[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] - Abstract
The French Society of Pathology (SFP) organized its first data challenge in 2020 with the help of the Health Data Hub (HDH). The organization of this event first consisted of recruiting nearly 5000 cervical biopsy slides obtained from 20 pathology centers. After ensuring that patients did not refuse to include their slides in the project, the slides were anonymized, digitized, and annotated by expert pathologists, and finally uploaded to a data challenge platform for competitors from around the world. Competing teams had to develop algorithms that could distinguish 4 diagnostic classes in cervical epithelial lesions. Among the many submissions from competitors, the best algorithms achieved an overall score close to 95%. The final part of the competition lasted only 6 weeks, and the goal of SFP and HDH is now to allow for the collection to be published in open access for the scientific community. In this report, we have performed a "post-competition analysis" of the results. We first described the algorithmic pipelines of 3 top competitors. We then analyzed several difficult cases that even the top competitors could not predict correctly. A medical committee of several expert pathologists looked for possible explanations for these erroneous results by reviewing the images, and we present their findings here targeted for a large audience of pathologists and data scientists in the field of digital pathology.
- Published
- 2022
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