Compositional Representation Learning for Brain Tumour Segmentation
Xiao Liu Antanas Kascenas Hannah Watson Sotirios A. Tsaftaris Alison Q. O'Neil
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).