Can AI predict epithelial lesion categories via automated analysis of cervical biopsies: The TissueNet challenge?

• Main Authors: Nicolas Loménie (Author), Capucine Bertrand (Author), Rutger H.J. Fick (Author), Saima Ben Hadj (Author), Brice Tayart (Author), Cyprien Tilmant (Author), Isabelle Farré (Author), Soufiane Z. Azdad (Author), Samy Dahmani (Author), Gilles Dequen (Author), Ming Feng (Author), Kele Xu (Author), Zimu Li (Author), Sophie Prevot (Author), Christine Bergeron (Author), Guillaume Bataillon (Author), Mojgan Devouassoux-Shisheboran (Author), Claire Glaser (Author), Agathe Delaune (Author), Séverine Valmary-Degano (Author), Philippe Bertheau (Author)
• Format: Book
• Published: Elsevier, 2022-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2153-3539; 10.1016/j.jpi.2022.100149
• Keywords: Artificial intelligence; Data challenge; Whole slide images; Uterine cervix; Computer applications to medicine. Medical informatics; R858-859.7; Pathology; RB1-214
• Source: Journal of Pathology Informatics, Vol 13, Iss , Pp 100149- (2022)

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.