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

Ruyi Dai; Tingkwong Ng; Hao Chen

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arXiv · 2604.23271

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

Ruyi Dai·Tingkwong Ng·Hao Chen

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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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Ruyi Dai, Tingkwong Ng, Hao Chen. 2026-04-25. A Hierarchical Ensemble Inference Pipeline for Robust White Blood Cell Classification Under Domain Shifts. https://arxiv.org/abs/2604.23271

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