构建首个白细胞像素级形态标注数据集，助力病理分析与可解释AI
WBCAtt+: Fine-Grained Pixel-Level Morphological Annotations for White Blood Cell Images
- 为白细胞图像添加11种形态属性和5类像素级组件标注
- 包含11.3万张图像标签与1万张分割图，规模领先
- 支持细粒度识别与可解释性模型，适合医学图像研究者
白细胞显微检查在病理学中至关重要，是诊断白血病、贫血等血液疾病的基础。现有数据集多仅标注细胞类别，缺乏病理学家用于判断的详细形态特征。为此，我们提出WBCAtt+，首个对白细胞图像进行密集标注的数据集，包含11种形态属性与5类像素级细胞组分。该数据集含11.3万张图像级标签和1万张分割图，为首个提供全面注释的白细胞数据集。基于此，我们建立了属性识别与语义分割的基线模型，并设计了考虑细胞结构组成属性识别模型，进一步提升识别性能。最后，展示了该数据集在可解释人工智能中的多种应用，如反事实示例生成。数据集与代码已公开。
原文摘要 · Abstract (English)
The microscopic examination of white blood cells (WBCs) plays a fundamental role in pathology and is essential for diagnosing blood disorders such as leukemia and anemia. To support further research on WBC images, multiple datasets have been proposed. However, they mainly annotate cell categories, and lack detailed morphological characteristics that pathologists use to explain their interpretations of cells. To address this gap, we introduce WBCAtt+, a novel dataset of WBC images densely annotated with 11 morphological attributes and five pixel-level cell components. With 113k image-level labels and 10k segmentation maps, WBCAtt+ is the first to provide comprehensive annotations for WBC images. Leveraging this dataset, we provide baseline models for attribute recognition and semantic segmentation. We also design an attribute recognition model to incorporate compositional structure of cells, further improving the recognition performance. Lastly, we showcase various applications enabled by our dataset, such as explainable AI models, including counterfactual example generation. \revision{The dataset and code are publicly available\footnote{https://doi.org/10.57967/hf/8143}}.
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