WBCAtt+: 细粒度像素级形态学标注用于白血球图像
WBCAtt+: Fine-Grained Pixel-Level Morphological Annotations for White Blood Cell Images
AI总结：
本文提出WBCAtt+数据集，通过11个形态学属性和5个像素级细胞组件的密集标注，为白血球图像提供了全面的标注，用于改进属性识别和语义分割的基准模型，并展示了可解释AI模型等应用。
摘要：
白血球（WBC）的显微检查在病理学中起着基础性作用，对于诊断如白血病和贫血等血液疾病至关重要。为了支持进一步的WBC图像研究，已提出多个数据集。然而，这些数据集主要标注细胞类别，缺乏病理学家用于解释细胞解释的详细形态学特征。为解决这一差距，我们引入WBCAtt+，一个包含11个形态学属性和5个像素级细胞组件的新型WBC图像数据集。WBCAtt+拥有113,000个图像级标签和10,000个分割图，是首个为WBC图像提供全面标注的数据集。利用此数据集，我们提供了属性识别和语义分割的基准模型。我们还设计了一个属性识别模型，以整合细胞的组成结构，进一步提高识别性能。最后，我们展示了由我们的数据集启用的各种应用，如可解释AI模型，包括反事实示例生成。
英文摘要：
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}}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
