Stain-aware Domain Alignment for Imbalance Blood Cell Classification

• URL: http://arxiv.org/abs/2412.02976v1
• Date: Wed, 04 Dec 2024 02:37:53 GMT
• Title: Stain-aware Domain Alignment for Imbalance Blood Cell Classification
• Authors: Yongcheng Li, Lingcong Cai, Ying Lu, Xianghua Fu, Xiao Han, Ma Li, Wenxing Lai, Xiangzhong Zhang, Xiaomao Fan,
• Abstract summary: We propose a novel blood cell classification method termed SADA via stain-aware domain alignment. The primary objective of this work is to mine domain-invariant features in the presence of domain shifts and data imbalances. Experiment results on four public blood cell datasets and a private real dataset collected from the Third Affiliated Hospital of Sun Yat-sen University.
• Score: 3.848381820421942
• License:
• Abstract: Blood cell identification is critical for hematological analysis as it aids physicians in diagnosing various blood-related diseases. In real-world scenarios, blood cell image datasets often present the issues of domain shift and data imbalance, posing challenges for accurate blood cell identification. To address these issues, we propose a novel blood cell classification method termed SADA via stain-aware domain alignment. The primary objective of this work is to mine domain-invariant features in the presence of domain shifts and data imbalances. To accomplish this objective, we propose a stain-based augmentation approach and a local alignment constraint to learn domain-invariant features. Furthermore, we propose a domain-invariant supervised contrastive learning strategy to capture discriminative features. We decouple the training process into two stages of domain-invariant feature learning and classification training, alleviating the problem of data imbalance. Experiment results on four public blood cell datasets and a private real dataset collected from the Third Affiliated Hospital of Sun Yat-sen University demonstrate that SADA can achieve a new state-of-the-art baseline, which is superior to the existing cutting-edge methods with a big margin. The source code can be available at the URL (\url{https://github.com/AnoK3111/SADA}).

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