Test Time Transform Prediction for Open Set Histopathological Image Recognition

Research output: Contribution to book/Conference proceedings/Anthology/ReportConference contributionContributedpeer-review

Contributors

  • Adrian Galdran - , Pompeu Fabra University, University of Adelaide (Author)
  • Katherine J. Hewitt - , RWTH Aachen University (Author)
  • Narmin Ghaffari Laleh - , RWTH Aachen University (Author)
  • Jakob N. Kather - , RWTH Aachen University (Author)
  • Gustavo Carneiro - , University of Adelaide (Author)
  • Miguel A. González Ballester - , Pompeu Fabra University, ICREA (Author)

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 languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings
EditorsLinwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li
PublisherSpringer Science and Business Media B.V.
Pages263-272
Number of pages10
ISBN (print)9783031164330
Publication statusPublished - 2022
Peer-reviewedYes
Externally publishedYes

Publication series

SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13432 LNCS
ISSN0302-9743

Conference

Title25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Duration18 - 22 September 2022
CitySingapore
CountrySingapore

Keywords

Sustainable Development Goals

Keywords

  • Histopathological image analysis, Open Set Recognition