Abstract

Prototypical Networks operate by embedding both support and query samples into a common feature space and then representing each class with the mean vector of its support embeddings. Yet, the inherent complexity of medical imagery poses significant challenges for isolating features that are both precise and dependable. Consequently, constructing effective prototypes in this domain demands not only sophisticated preprocessing and more powerful embedding architectures, but also deliberate refinement of feature representations. In this context, most important and representative feature map selection is critical. We introduce Selective Feature Representation in Prototypical Networks, a lightweight yet effective enhancement to prototype-based few-shot learning. Proposed approach explicitly refines support embeddings by ranking and selecting the top feature maps for each class, leveraging an ensemble of channel-wise statistics—Global Average Pooling, Max Pooling, and Variance. Built on a compact CONV4 backbone, proposed method outperforms much larger state-of-the-art models on two medical benchmarks: achieving 67.18% (1-shot) and 78.20% (5-shot) on Derm7pt skin-lesion classification, and 63.39% (1-shot), 77.17% (5-shot), and 83.06% (10-shot) on BloodMNIST pathology classification. These gains demonstrate that targeted feature-map selection significantly improves prototype quality and generalization with minimal complexity, offering a practical solution for resource-constrained clinical applications.

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