Test Time Transform Prediction for Open Set Histopathological Image Recognition
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
Abstract
Tissue typology annotation in Whole Slide histological images is a complex and tedious, yet necessary task for the development of computational pathology models. We propose to address this problem by applying Open Set Recognition techniques to the task of jointly classifying tissue that belongs to a set of annotated classes, e.g. clinically relevant tissue categories, while rejecting in test time Open Set samples, i.e. images that belong to categories not present in the training set. To this end, we introduce a new approach for Open Set histopathological image recognition based on training a model to accurately identify image categories and simultaneously predict which data augmentation transform has been applied. In test time, we measure model confidence in predicting this transform, which we expect to be lower for images in the Open Set. We carry out comprehensive experiments in the context of colorectal cancer assessment from histological images, which provide evidence on the strengths of our approach to automatically identify samples from unknown categories. Code is released at https://github.com/agaldran/t3po.
Details
Original language | English |
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Title of host publication | Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings |
Editors | Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li |
Publisher | Springer Science and Business Media B.V. |
Pages | 263-272 |
Number of pages | 10 |
ISBN (print) | 9783031164330 |
Publication status | Published - 2022 |
Peer-reviewed | Yes |
Externally published | Yes |
Publication series
Series | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 13432 LNCS |
ISSN | 0302-9743 |
Conference
Title | 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 |
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Duration | 18 - 22 September 2022 |
City | Singapore |
Country | Singapore |
Keywords
Sustainable Development Goals
ASJC Scopus subject areas
Keywords
- Histopathological image analysis, Open Set Recognition