Paper
Robust White Blood Cell Classification with Stain-Normalized Decoupled Learning and Ensembling
Authors
Luu Le, Hoang-Loc Cao, Ha-Hieu Pham, Thanh-Huy Nguyen, Ulas Bagci
Abstract
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.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.01976v1</id>\n <title>Robust White Blood Cell Classification with Stain-Normalized Decoupled Learning and Ensembling</title>\n <updated>2026-03-02T15:31:25Z</updated>\n <link href='https://arxiv.org/abs/2603.01976v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.01976v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CV'/>\n <published>2026-03-02T15:31:25Z</published>\n <arxiv:primary_category term='cs.CV'/>\n <author>\n <name>Luu Le</name>\n </author>\n <author>\n <name>Hoang-Loc Cao</name>\n </author>\n <author>\n <name>Ha-Hieu Pham</name>\n </author>\n <author>\n <name>Thanh-Huy Nguyen</name>\n </author>\n <author>\n <name>Ulas Bagci</name>\n </author>\n </entry>"
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