Search

Your search keyword '"Rutger H.J. Fick"' showing total 2 results
Start Over Author "Rutger H.J. Fick"
2 results on '"Rutger H.J. Fick"'

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

Author

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, and Philippe Bertheau

Subjects
Artificial intelligence, Data challenge, Whole slide images, Uterine cervix, Computer applications to medicine. Medical informatics, R858-859.7, Pathology, RB1-214
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
Full Text
View/download PDF

2. Mitosis domain generalization in histopathology images - The MIDOG challenge

Author

Marc Aubreville, Nikolas Stathonikos, Christof A. Bertram, Robert Klopfleisch, Natalie ter Hoeve, Francesco Ciompi, Frauke Wilm, Christian Marzahl, Taryn A. Donovan, Andreas Maier, Jack Breen, Nishant Ravikumar, Youjin Chung, Jinah Park, Ramin Nateghi, Fattaneh Pourakpour, Rutger H.J. Fick, Saima Ben Hadj, Mostafa Jahanifar, Adam Shephard, Jakob Dexl, Thomas Wittenberg, Satoshi Kondo, Maxime W. Lafarge, Viktor H. Koelzer, Jingtang Liang, Yubo Wang, Xi Long, Jingxin Liu, Salar Razavi, April Khademi, Sen Yang, Xiyue Wang, Ramona Erber, Andrea Klang, Karoline Lipnik, Pompei Bolfa, Michael J. Dark, Gabriel Wasinger, Mitko Veta, and Katharina Breininger

Subjects
FOS: Computer and information sciences, Radiological and Ultrasound Technology, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, FOS: Physical sciences, Health Informatics, Electrical Engineering and Systems Science - Image and Video Processing, Physics - Medical Physics, Quantitative Biology - Quantitative Methods, Computer Graphics and Computer-Aided Design, FOS: Biological sciences, FOS: Electrical engineering, electronic engineering, information engineering, Radiology, Nuclear Medicine and imaging, Computer Vision and Pattern Recognition, Medical Physics (physics.med-ph), Quantitative Methods (q-bio.QM), Rare cancers Radboud Institute for Health Sciences [Radboudumc 9]
Abstract

The density of mitotic figures within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of mitotic figures by pathologists is known to be subject to a strong inter-rater bias, which limits the prognostic value. State-of-the-art deep learning methods can support the expert in this assessment but are known to strongly deteriorate when applied in a different clinical environment than was used for training. One decisive component in the underlying domain shift has been identified as the variability caused by using different whole slide scanners. The goal of the MICCAI MIDOG 2021 challenge has been to propose and evaluate methods that counter this domain shift and derive scanner-agnostic mitosis detection algorithms. The challenge used a training set of 200 cases, split across four scanning systems. As a test set, an additional 100 cases split across four scanning systems, including two previously unseen scanners, were given. The best approaches performed on an expert level, with the winning algorithm yielding an F_1 score of 0.748 (CI95: 0.704-0.781). In this paper, we evaluate and compare the approaches that were submitted to the challenge and identify methodological factors contributing to better performance., Comment: 19 pages, 9 figures, summary paper of the 2021 MICCAI MIDOG challenge

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results