# Robust White Blood Cell Classification with Stain-Normalized Decoupled Learning and Ensembling

arXiv: https://arxiv.org/abs/2603.01976 · HTML: https://mlsanity.com/paper/2603.01976/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 2 Mar 2026.

## Builds on (most cited first, 5 of 5)

- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Decoupling Representation and Classifier for Long-Tailed Recognition](https://mlsanity.com/paper/1910.09217/) (2019)
- [Handling Supervision Scarcity in Chest X-ray Classification: Long-Tailed and Zero-Shot Learning](https://mlsanity.com/paper/2602.13430/) (2026)
- [Domain-invariant Mixed-domain Semi-supervised Medical Image Segmentation with Clustered Maximum Mean Discrepancy Alignment](https://mlsanity.com/paper/2601.16954/) (2026)

## Built on by (most cited first, 1 of 1)

- [WBCBench 2026: A Challenge for Robust White Blood Cell Classification Under Class Imbalance](https://mlsanity.com/paper/2604.10797/) (2026)

## Abstract (arXiv)

White blood cell (WBC) classification is fundamental for hematology applications such as infection assessment, leukemia screening, and treatment monitoring. However, real-world WBC datasets present substantial appearance variations caused by staining and scanning conditions, as well as severe class imbalance in which common cell types dominate while rare but clinically important categories are underrepresented. To address these challenges, we propose a stain-normalized, decoupled training framework that first learns transferable representations using instance-balanced sampling, and then rebalances the classifier with class-aware sampling and a hybrid loss combining effective-number weighting and focal modulation. In inference stage, we further enhance robustness by ensembling various trained backbones with test-time augmentation. Our approach achieved the top rank on the leaderboard of the WBCBench 2026: Robust White Blood Cell Classification Challenge at ISBI 2026.