Stain-aware Domain Alignment for Imbalance Blood Cell Classification
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913596680699904 |
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| author | Li, Yongcheng Cai, Lingcong Lu, Ying Fu, Xianghua Han, Xiao Li, Ma Lai, Wenxing Zhang, Xiangzhong Fan, Xiaomao |
| author_facet | Li, Yongcheng Cai, Lingcong Lu, Ying Fu, Xianghua Han, Xiao Li, Ma Lai, Wenxing Zhang, Xiangzhong Fan, Xiaomao |
| contents | 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}). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02976 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Stain-aware Domain Alignment for Imbalance Blood Cell Classification Li, Yongcheng Cai, Lingcong Lu, Ying Fu, Xianghua Han, Xiao Li, Ma Lai, Wenxing Zhang, Xiangzhong Fan, Xiaomao Computer Vision and Pattern Recognition 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}). |
| title | Stain-aware Domain Alignment for Imbalance Blood Cell Classification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.02976 |