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Synergizing Deep Learning and Biological Heuristics for Extreme Long-Tail White Blood Cell Classification

arXiv cs.CV / 3/18/2026

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Key Points

  • The authors propose a hybrid framework for rare-class generalization that combines a 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.
  • The approach targets extreme long-tail distributions, class imbalance, and domain shift in automated white blood cell classification to prevent overfitting to dominant classes and improve performance on rare subtypes.
  • On the WBCBench 2026 challenge, the method achieves a Macro-F1 of 0.77139 on the private leaderboard, demonstrating strong performance under severe imbalance.
  • The work highlights the value of incorporating biological priors into deep learning for hematological image analysis, suggesting a productive synergy between domain knowledge and AI.
  • The pipeline’s components—artifact removal, contrastive representation learning, and morphology-informed refinement—collectively enhance generalization to unseen distributions in medical imaging tasks.

Abstract

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 challenge, our method achieves a Macro-F1 of 0.77139 on the private leaderboard, demonstrating strong performance under severe imbalance and highlighting the value of incorporating biological priors into deep learning for hematological image analysis.

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