Can I read vMFNet: Compositionality Meets Domain-generalised Segmentation on EtoBox?

vMFNet: Compositionality Meets Domain-generalised Segmentation by Liu, Xiao; Thermos, Spyridon; Sanchez, Pedro; O'Neil, Alison Q.; Tsaftaris, Sotirios A. is a scholarly article available to read on EtoBox.

What is vMFNet: Compositionality Meets Domain-generalised Segmentation about?

Training medical image segmentation models usually requires a large amount of labeled data. By contrast, humans can quickly learn to accurately recognise anatomy of interest from medical (e.g. MRI and CT) images with some limited guidance. Such recognition ability can easily generalise to new images from different clinical centres. This rapid and generalisable learning ability is mostly due to the compositional structure of image patterns in the human brain, which is less incorporated in medical image segmentation. In this paper, we model the compositional components (i.e. patterns) of human anatomy as learnable von-Mises-Fisher (vMF) kernels, which are robust to images collected from different domains (e.g. clinical centres). The image features can be decomposed to (or composed by) the components with the composing operations, i.e. the vMF likelihoods. The vMF likelihoods tell how likely each anatomical part is at each position of the image. Hence, the segmentation mask can be predicted based on the vMF likelihoods. Moreover, with a reconstruction module, unlabeled data can also be used to learn the vMF kernels and likelihoods by recombining them to reconstruct the input image. Ex

Author

Liu, Xiao; Thermos, Spyridon; Sanchez, Pedro; O'Neil, Alison Q.; Tsaftaris, Sotirios A.

Published

2022

Language

EN