针对染色差异和数据不平衡，提出新方法提升血细胞分类准确率
Stain-aware Domain Alignment for Imbalance Blood Cell Classification
- 基于染色特性设计增强策略与局部对齐约束，学习跨域不变特征
- 在4个公开数据集和1个真实医院数据集上均达到最新最好结果
- 适合医学图像分析、病理诊断等需要处理数据偏移的场景
血细胞识别对血液疾病诊断至关重要。现实中血细胞图像数据常存在域偏移和数据不平衡问题，影响识别准确性。本文提出SADA方法，通过染色感知域对齐，挖掘域不变特征。设计基于染色的增强方法与局部对齐约束，结合域不变监督对比学习，有效提取判别性特征。将训练分为域不变特征学习与分类训练两阶段，缓解数据不平衡。在四个公开血细胞数据集及中山大学附属第三医院采集的私有数据集上验证，SADA性能显著优于现有先进方法，达到新基准。
原文摘要 · Abstract (English)
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}).
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。