A Hierarchical Ensemble Inference Pipeline for Robust White Blood Cell Classification Under Domain Shifts

• URL: http://arxiv.org/abs/2604.23271v1
• Date: Sat, 25 Apr 2026 12:25:46 GMT
• Title: A Hierarchical Ensemble Inference Pipeline for Robust White Blood Cell Classification Under Domain Shifts
• Abstract summary: We propose a memory-augmented, hierarchical ensemble pipeline for WBC classification under domain shifts.<n>Our method ranks within the top ten by macro F1-score in the final testing phase.
• Score: 5.200916021795385
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated white blood cell (WBC) classification is essential for scalable leukaemia screening. However, real-world deployment is challenged by domain shifts caused by staining protocols, scanner characteristics, and inter-laboratory variability, which often degrade model performance. The White Blood Cell Classification Challenge (WBCBench) at ISBI 2026 aims to advance robust WBC recognition, with a focus on accurately identifying blast cells and other clinically critical rare subtypes. We propose a memory-augmented, hierarchical ensemble pipeline for WBC classification under domain shifts, leveraging a feature bank and a DinoBloom backbone fine-tuned with LoRA. Our three-stage inference hierarchy combines k-nearest neighbors (kNN) retrieval at each level, reducing over-reliance on any single decision. Evaluated on the WBCBench dataset, our method ranks within the top ten by macro F1-score in the final testing phase.

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