Ensemble of Small Classifiers For Imbalanced White Blood Cell Classification

• URL: http://arxiv.org/abs/2603.20856v1
• Date: Sat, 21 Mar 2026 15:24:52 GMT
• Title: Ensemble of Small Classifiers For Imbalanced White Blood Cell Classification
• Abstract summary: We propose a lightweight ensemble approach for classification of cells during Haematopoiesis.<n>We show that a simple ensemble of lightweight pretrained SwinV2-Tiny, DinoBloom-Small and ConvNeXT-V2-Tiny models achieves excellent performance on this challenging dataset.
• Score: 9.420673339712211
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Automating white blood cell classification for diagnosis of leukaemia is a promising alternative to time-consuming and resource-intensive examination of cells by expert pathologists. However, designing robust algorithms for classification of rare cell types remains challenging due to variations in staining, scanning and inter-patient heterogeneity. We propose a lightweight ensemble approach for classification of cells during Haematopoiesis, with a focus on the biology of Granulopoiesis, Monocytopoiesis and Lymphopoiesis. Through dataset expansion to alleviate some class imbalance, we demonstrate that a simple ensemble of lightweight pretrained SwinV2-Tiny, DinoBloom-Small and ConvNeXT-V2-Tiny models achieves excellent performance on this challenging dataset. We train 3 instantiations of each architecture in a stratified 3-fold cross-validation framework; for an input image, we forward-pass through all 9 models and aggregate through logit averaging. We further reason on the weaknesses of our model in confusing similar-looking myelocytes in granulopoiesis and lymphocytes in lymphopoiesis. Code: https://gitlab.com/siddharthsrivastava/wbc-bench-2026.

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