Compositional Representation Learning for Brain Tumour Segmentation

Xiao Liu*, Antanas Kascenas, Hannah Watson, Sotirios A. Tsaftaris, Alison Q. O’Neil

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

For brain tumour segmentation, deep learning models can achieve human expert-level performance given a large amount of data and pixel-level annotations. However, the expensive exercise of obtaining pixel-level annotations for large amounts of data is not always feasible, and performance is often heavily reduced in a low-annotated data regime. To tackle this challenge, we adapt a mixed supervision framework, vMFNet, to learn robust compositional representations using unsupervised learning and weak supervision alongside non-exhaustive pixel-level pathology labels. In particular, we use the BraTS dataset to simulate a collection of 2-point expert pathology annotations indicating the top and bottom slice of the tumour (or tumour sub-regions: peritumoural edema, GD-enhancing tumour, and the necrotic/non-enhancing tumour) in each MRI volume, from which weak image-level labels that indicate the presence or absence of the tumour (or the tumour sub-regions) in the image are constructed. Then, vMFNet models the encoded image features with von-Mises-Fisher (vMF) distributions, via learnable and compositional vMF kernels which capture information about structures in the images. We show that good tumour segmentation performance can be achieved with a large amount of weakly labelled data but only a small amount of fully-annotated data. Interestingly, emergent learning of anatomical structures occurs in the compositional representation even given only supervision relating to pathology (tumour).

Original languageEnglish
Title of host publicationDomain Adaptation and Representation Transfer - 5th MICCAI Workshop, DART 2023, Held in Conjunction with MICCAI 2023, Proceedings
EditorsLisa Koch, M. Jorge Cardoso, Enzo Ferrante, Konstantinos Kamnitsas, Mobarakol Islam, Meirui Jiang, Nicola Rieke, Sotirios A. Tsaftaris, Dong Yang
PublisherSpringer
Pages41-51
Number of pages11
ISBN (Print)9783031458569
DOIs
Publication statusPublished - 14 Oct 2023
Event5th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2023 - Vancouver, Canada
Duration: 12 Oct 202312 Oct 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14293 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2023
Country/TerritoryCanada
CityVancouver
Period12/10/2312/10/23

Keywords / Materials (for Non-textual outputs)

  • Brain tumour segmentation
  • Compositionality
  • Representation learning
  • Semi-supervised
  • Weakly-supervised

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