WBCBench 2026: A Challenge for Robust White Blood Cell Classification Under Class Imbalance
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910122959175680 |
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| author | Tian, Xin Ma, Xudong Yang, Tianqi Achim, Alin Papież, Bartłomiej W Watanaboonyongcharoen, Phandee Anantrasirichai, Nantheera |
| author_facet | Tian, Xin Ma, Xudong Yang, Tianqi Achim, Alin Papież, Bartłomiej W Watanaboonyongcharoen, Phandee Anantrasirichai, Nantheera |
| contents | We present WBCBench 2026, an ISBI challenge and benchmark for automated WBC classification designed to stress-test algorithms under three key difficulties: (i) severe class imbalance across 13 morphologically fine-grained WBC classes, (ii) strict patient-level separation between training, validation and test sets, and (iii) synthetic scanner- and setting-induced domain shift via controlled noise, blur and illumination perturbations. All images are single-site microscopic blood smear acquisitions with standardised staining and expert hematopathologist annotations. This paper reviews the challenge and summarises the proposed solutions and final outcomes. The benchmark is organised into two phases. Phase 1 provides a pristine training set. Phase 2 introduces degraded images with split-specific severity distributions for train, validation and test, emulating a realistic shift between development and deployment conditions. We specify a standardised submission schema, open-source evaluator, and macro-averaged F1 score as the primary ranking metric. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_10797 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | WBCBench 2026: A Challenge for Robust White Blood Cell Classification Under Class Imbalance Tian, Xin Ma, Xudong Yang, Tianqi Achim, Alin Papież, Bartłomiej W Watanaboonyongcharoen, Phandee Anantrasirichai, Nantheera Computer Vision and Pattern Recognition We present WBCBench 2026, an ISBI challenge and benchmark for automated WBC classification designed to stress-test algorithms under three key difficulties: (i) severe class imbalance across 13 morphologically fine-grained WBC classes, (ii) strict patient-level separation between training, validation and test sets, and (iii) synthetic scanner- and setting-induced domain shift via controlled noise, blur and illumination perturbations. All images are single-site microscopic blood smear acquisitions with standardised staining and expert hematopathologist annotations. This paper reviews the challenge and summarises the proposed solutions and final outcomes. The benchmark is organised into two phases. Phase 1 provides a pristine training set. Phase 2 introduces degraded images with split-specific severity distributions for train, validation and test, emulating a realistic shift between development and deployment conditions. We specify a standardised submission schema, open-source evaluator, and macro-averaged F1 score as the primary ranking metric. |
| title | WBCBench 2026: A Challenge for Robust White Blood Cell Classification Under Class Imbalance |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.10797 |