Abstract
Leukemia is 10th most frequently diagnosed cancer and one of the leading causes of cancer related deaths worldwide. Realistic analysis of Leukemia requires White Blook Cells (WBC) localization, classification, and morphological assessment. Despite deep learning advances in medical imaging, leukemia analysis lacks a large, diverse multi-task dataset, while existing small datasets lack domain diversity, limiting real world applicability. To overcome dataset challenges, we present a large scale WBC dataset named Large Leukemia Dataset (LLD) and novel methods for detecting WBC with their attributes. Our contribution here is threefold. First, we present a large-scale Leukemia dataset collected through Peripheral Blood Films (PBF) from several patients, through multiple microscopes, multi cameras, and multi magnification. To enhance diagnosis explainability and medical expert acceptance, each leukemia cell is annotated at 100x with 7 morphological attributes, ranging from Cell Size to Nuclear Shape. Secondly, we propose a multi task model that not only detects WBCs but also predicts their attributes, providing an interpretable and clinically meaningful solution. Third, we propose a method for WBC detection with attribute analysis using sparse annotations. This approach reduces the annotation burden on hematologists, requiring them to mark only a small area within the field of view. Our method enables the model to leverage the entire field of view rather than just the annotated regions, enhancing learning efficiency and diagnostic accuracy. From diagnosis explainability to overcoming domain shift challenges, presented datasets could be used for many challenging aspects of microscopic image analysis. The datasets, code, and demo are available at: this https URL
Abstract (translated)
白血病是全球诊断频率最高的第十种癌症，也是导致癌症相关死亡的主要原因之一。对白血病进行现实分析需要定位、分类和评估白细胞（WBC）的形态特征。尽管深度学习在医学影像方面的进展显著，但白血病分析却缺乏大规模且多任务类型的多样化数据集；现有的小规模数据集也因领域多样性不足而限制了其实际应用。 为克服这些数据集挑战，我们推出了一项名为“大型白血病数据集”（Large Leukemia Dataset, LLD）的项目，并提出一种检测WBC及其属性的新方法。我们的贡献主要有三个方面： 1. 我们收集了一个大规模的数据集——LLD，该数据集通过外周血涂片（PBF）从多个患者处获取，在不同显微镜、相机和放大倍数下拍摄。为了增强诊断的解释性和医学专家的认可度，每个白血病细胞都在100x下标注了7个形态特征，涵盖了细胞大小到核形状等多个方面。 2. 我们提出了一种多任务模型，不仅能够检测WBCs，还能预测它们的属性，从而提供了一个可解释且具有临床意义的解决方案。 3. 我们提出了一个使用稀疏注释进行WBC检测及其属性分析的方法。这种方法减少了血液学家在标注过程中的负担，只需标记视野内的一个小区域即可完成任务。我们的方法使模型能够利用整个视野的信息，而不仅仅是已标注的区域，从而提高了学习效率和诊断准确性。 从增强诊断解释性到克服领域转移挑战，“所提出的”数据集可用于显微图像分析中许多具有挑战性的方面。“这些数据集、代码和演示可以在这里获取：[此链接](https://this-url.com/)（请将“this https URL”替换为实际的网址）。
URL
https://arxiv.org/abs/2504.02602
