Test Time Transform Prediction for Open Set Histopathological Image Recognition

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Beitragende

  • Adrian Galdran - , Universitat Pompeu Fabra, University of Adelaide (Autor:in)
  • Katherine J. Hewitt - , Rheinisch-Westfälische Technische Hochschule Aachen (Autor:in)
  • Narmin Ghaffari Laleh - , Rheinisch-Westfälische Technische Hochschule Aachen (Autor:in)
  • Jakob N. Kather - , Rheinisch-Westfälische Technische Hochschule Aachen (Autor:in)
  • Gustavo Carneiro - , University of Adelaide (Autor:in)
  • Miguel A. González Ballester - , Universitat Pompeu Fabra, ICREA (Autor:in)

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

OriginalspracheEnglisch
TitelMedical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings
Redakteure/-innenLinwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li
Herausgeber (Verlag)Springer Science and Business Media B.V.
Seiten263-272
Seitenumfang10
ISBN (Print)9783031164330
PublikationsstatusVeröffentlicht - 2022
Peer-Review-StatusJa
Extern publiziertJa

Publikationsreihe

ReiheLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band13432 LNCS
ISSN0302-9743

Konferenz

Titel25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Dauer18 - 22 September 2022
StadtSingapore
LandSingapur

Schlagworte

Ziele für nachhaltige Entwicklung

Schlagwörter

  • Histopathological image analysis, Open Set Recognition