Correlation Weighted Prototype-based Self-Supervised One-Shot Segmentation of Medical Images
This addresses the problem of medical image segmentation with scarce annotations for clinicians and researchers, representing an incremental improvement over existing prototype-based methods.
The paper tackles medical image segmentation with limited annotated data by proposing a prototype-based self-supervised one-shot learning framework that uses correlation-weighted prototypes and quadrant masking, achieving performance comparable to state-of-the-art methods on abdominal CT and MR datasets.
Medical image segmentation is one of the domains where sufficient annotated data is not available. This necessitates the application of low-data frameworks like few-shot learning. Contemporary prototype-based frameworks often do not account for the variation in features within the support and query images, giving rise to a large variance in prototype alignment. In this work, we adopt a prototype-based self-supervised one-way one-shot learning framework using pseudo-labels generated from superpixels to learn the semantic segmentation task itself. We use a correlation-based probability score to generate a dynamic prototype for each query pixel from the bag of prototypes obtained from the support feature map. This weighting scheme helps to give a higher weightage to contextually related prototypes. We also propose a quadrant masking strategy in the downstream segmentation task by utilizing prior domain information to discard unwanted false positives. We present extensive experimentations and evaluations on abdominal CT and MR datasets to show that the proposed simple but potent framework performs at par with the state-of-the-art methods.