WBCBench 2026: A Challenge for Robust White Blood Cell Classification Under Class Imbalance

• URL: http://arxiv.org/abs/2604.10797v1
• Date: Sun, 12 Apr 2026 20:04:26 GMT
• Title: WBCBench 2026: A Challenge for Robust White Blood Cell Classification Under Class Imbalance
• Abstract summary: WBCBench 2026 is an I SBI challenge and benchmark for automated WBC classification.<n>This paper reviews the challenge and summarises the proposed solutions and final outcomes.<n>We specify a standardised submission schema, open-source evaluator, and macro-averaged F1 score as the primary ranking metric.
• Score: 10.424924450197137
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• A Hierarchical Ensemble Inference Pipeline for Robust White Blood Cell Classification Under Domain Shifts [5.200916021795385]
  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.
  arXiv  Detail & Related papers  (2026-04-25T12:25:46Z)
• Fair Lung Disease Diagnosis from Chest CT via Gender-Adversarial Attention Multiple Instance Learning [6.302369456012738]
  We present a fairness-aware framework for multi-class lung disease diagnosis from chest CT volumes, developed for the Fair Disease Diagnosis Challenge.<n>The challenge requires classifying CT scans into four categories -- Healthy, COVID-19, Adenocarcinoma, and Squamous Cell Carcinoma.<n>Our approach addresses two core difficulties: the sparse pathological signal across hundreds of slices, and a severe demographic imbalance compounded across disease class and gender.
  arXiv  Detail & Related papers  (2026-03-13T13:42:52Z)
• Robust White Blood Cell Classification with Stain-Normalized Decoupled Learning and Ensembling [6.750164263871006]
  White blood cell (WBC) classification is fundamental for hematology applications such as infection assessment, leukemia screening, and treatment monitoring.<n>We propose a stain-normalized, decoupled training framework that learns transferable representations using instance-balanced sampling.<n>In inference stage, we further enhance robustness by ensembling various trained backbones with test-time augmentation.
  arXiv  Detail & Related papers  (2026-03-02T15:31:25Z)
• SG-CLDFF: A Novel Framework for Automated White Blood Cell Classification and Segmentation [0.0]
  Saliency-Guided Cross-Layer Deep Feature Fusion framework (SG-CLDFF)<n>A lightweight hybrid backbone (Swin-style) produces multi-resolution representations, which are fused by a ResNeXt-CCinspired cross-layer fusion module.<n>Interpretability is enforced through Grad-CAM visualizations and saliency consistency checks, allowing model decisions to be inspected at the regional level.
  arXiv  Detail & Related papers  (2025-10-20T08:07:39Z)
• EndoCIL: A Class-Incremental Learning Framework for Endoscopic Image Classification [5.574295682041076]
  Class-incremental learning (CIL) for endoscopic image analysis is crucial for real-world clinical applications.<n>We propose EndoCIL, a novel and unified CIL framework specifically tailored for endoscopic image diagnosis.
  arXiv  Detail & Related papers  (2025-10-20T06:26:54Z)
• Iterative Online Image Synthesis via Diffusion Model for Imbalanced Classification [29.730360798234294]
  We introduce an Iterative Online Image Synthesis framework to address the class imbalance problem in medical image classification. Our framework incorporates two key modules, namely Online Image Synthesis (OIS) and Accuracy Adaptive Sampling (AAS) To evaluate the effectiveness of our proposed method in addressing imbalanced classification, we conduct experiments on the HAM10000 and APTOS datasets.
  arXiv  Detail & Related papers  (2024-03-13T10:51:18Z)
• Balanced Classification: A Unified Framework for Long-Tailed Object Detection [74.94216414011326]
  Conventional detectors suffer from performance degradation when dealing with long-tailed data due to a classification bias towards the majority head categories. We introduce a unified framework called BAlanced CLassification (BACL), which enables adaptive rectification of inequalities caused by disparities in category distribution. BACL consistently achieves performance improvements across various datasets with different backbones and architectures.
  arXiv  Detail & Related papers  (2023-08-04T09:11:07Z)
• Learning Discriminative Representation via Metric Learning for Imbalanced Medical Image Classification [52.94051907952536]
  We propose embedding metric learning into the first stage of the two-stage framework specially to help the feature extractor learn to extract more discriminative feature representations. Experiments mainly on three medical image datasets show that the proposed approach consistently outperforms existing onestage and two-stage approaches.
  arXiv  Detail & Related papers  (2022-07-14T14:57:01Z)
• PCCT: Progressive Class-Center Triplet Loss for Imbalanced Medical Image Classification [55.703445291264]
  Imbalanced training data is a significant challenge for medical image classification. We propose a novel Progressive Class-Center Triplet (PCCT) framework to alleviate the class imbalance issue. The PCCT framework works effectively for medical image classification with imbalanced training images.
  arXiv  Detail & Related papers  (2022-07-11T11:43:51Z)
• Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation [64.59521853145368]
  Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event. To augment the labeled data and improve the generalization ability of the classification model, it is necessary to aggregate data from multiple sites. This task faces several challenges including class imbalance between mild and severe infections, domain distribution discrepancy between sites, and presence of heterogeneous features.
  arXiv  Detail & Related papers  (2021-09-08T07:56:51Z)
• Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without Forgetting [86.56447683502951]
  We propose a three-stage framework that allows to explicitly and effectively address these challenges. We evaluate the proposed framework on four challenging benchmark datasets for image and video few-shot classification.
  arXiv  Detail & Related papers  (2021-08-18T14:21:43Z)
• Multi-label Thoracic Disease Image Classification with Cross-Attention Networks [65.37531731899837]
  We propose a novel scheme of Cross-Attention Networks (CAN) for automated thoracic disease classification from chest x-ray images. We also design a new loss function that beyond cross-entropy loss to help cross-attention process and is able to overcome the imbalance between classes and easy-dominated samples within each class.
  arXiv  Detail & Related papers  (2020-07-21T14:37:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.