Partition-A-Medical-Image: Extracting Multiple Representative
  Sub-regions for Few-shot Medical Image Segmentation
Yazhou Zhu Shidong Wang Tong Xin Zheng Zhang Haofeng Zhang
Few-shot Medical Image Segmentation (FSMIS) is a more promising solution for
medical image segmentation tasks where high-quality annotations are naturally
scarce. However, current mainstream methods primarily focus on extracting
holistic representations from support images with large intra-class variations
in appearance and background, and encounter difficulties in adapting to query
images. In this work, we present an approach to extract multiple representative
sub-regions from a given support medical image, enabling fine-grained selection
over the generated image regions. Specifically, the foreground of the support
image is decomposed into distinct regions, which are subsequently used to
derive region-level representations via a designed Regional Prototypical
Learning (RPL) module. We then introduce a novel Prototypical Representation
Debiasing (PRD) module based on a two-way elimination mechanism which
suppresses the disturbance of regional representations by a self-support,
Multi-direction Self-debiasing (MS) block, and a support-query, Interactive
Debiasing (ID) block. Finally, an Assembled Prediction (AP) module is devised
to balance and integrate predictions of multiple prototypical representations
learned using stacked PRD modules. Results obtained through extensive
experiments on three publicly accessible medical imaging datasets demonstrate
consistent improvements over the leading FSMIS methods. The source code is
available at https://github.com/YazhouZhu19/PAMI.