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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 language | English |
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Title of host publication | Domain Adaptation and Representation Transfer - 5th MICCAI Workshop, DART 2023, Held in Conjunction with MICCAI 2023, Proceedings |
Editors | Lisa Koch, M. Jorge Cardoso, Enzo Ferrante, Konstantinos Kamnitsas, Mobarakol Islam, Meirui Jiang, Nicola Rieke, Sotirios A. Tsaftaris, Dong Yang |
Publisher | Springer |
Pages | 41-51 |
Number of pages | 11 |
ISBN (Print) | 9783031458569 |
DOIs | |
Publication status | Published - 14 Oct 2023 |
Event | 5th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2023 - Vancouver, Canada Duration: 12 Oct 2023 → 12 Oct 2023 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 14293 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 5th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2023 |
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Country/Territory | Canada |
City | Vancouver |
Period | 12/10/23 → 12/10/23 |
Keywords / Materials (for Non-textual outputs)
- Brain tumour segmentation
- Compositionality
- Representation learning
- Semi-supervised
- Weakly-supervised
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