Synergizing Deep Learning and Biological Heuristics for Extreme Long-Tail White Blood Cell Classification
Automated white blood cell (WBC) classification is essential for leukemia screening but remains challenged by extreme class imbalance, long-tail distributions, and domain shift, leading deep models to overfit dominant classes and fail on rare subtypes. We propose a hybrid framework for rare-class generalization that integrates a generative Pix2Pix-based restoration module for artifact removal, a Swin Transformer ensemble with MedSigLIP contrastive embeddings for robust representation learning, and a biologically-inspired refinement step using geometric spikiness and Mahalanobis-based morphological constraints to recover out-of-distribution predictions. Evaluated on the WBCBench 2026 chall...
| Authors | Duc T. Nguyen, Hoang-Long Nguyen, Huy-Hieu Pham |
|---|---|
| Primary category | cs.CV |
| Published | 17 March 2026 |
| Last updated at source | 28 March 2026 |
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