精确且快速的双核细胞（BC）检测在预测白血病和其他恶性肿瘤的风险中起着重要作用。然而，使用显微镜图像手动计数BC是耗时且主观的。此外，由于染色质量的限制和BC显微镜切片图像中形态特征的多样性，传统的图像处理方法在BC整切片图像中的效果不佳。为了克服这一挑战，我们提出了一种基于深度学习和BC结构先验的多任务方法，该方法级联实现了WSI级别的BC粗检测和图块级别的细粒度分类。粗检测网络是基于圆形边界框进行细胞检测和以核心关键点进行核检测的多任务检测框架。圆形边界框的表示方式降低了自由度，相对于常规矩形边界框减轻了周围杂质的影响，并且在WSI中具有旋转不变性。在细胞核中检测关键点可以辅助网络的感知，并用于后续细粒度分类中的无监督颜色层分割。细粒度分类网络包括基于颜色层掩模监督的背景区域抑制模块和基于变换器的关键区域选择模块，其具有全局建模能力。此外，首次提出了一种无监督和无对应的细胞质生成网络，以扩展长尾分布数据集。最后，在BC多中心数据集上进行了实验。所提出的BC细粒度检测方法在几乎所有评价指标中优于其他基准方法，为癌症筛查等任务提供了澄清和支持。版权所有© 2023 Elsevier B.V. 保留所有权利。

Accurate and quick binuclear cell (BC) detection plays a significant role in predicting the risk of leukemia and other malignant tumors. However, manual counting of BCs using microscope images is time consuming and subjective. Moreover, traditional image processing approaches perform poorly due to the limitations in staining quality and the diversity of morphological features in binuclear cell (BC) microscopy whole-slide images (WSIs). To overcome this challenge, we propose a multi-task method inspired by the structure prior of BCs based on deep learning, which cascades to implement BC coarse detection at the WSI level and fine-grained classification at the patch level. The coarse detection network is a multitask detection framework based on circular bounding boxes for cell detection and central key points for nucleus detection. Circle representation reduces the degrees of freedom, mitigates the effect of surrounding impurities compared to usual rectangular boxes and can be rotation invariant in WSIs. Detecting key points in the nucleus can assist in network perception and be used for unsupervised color layer segmentation in later fine-grained classification. The fine classification network consists of a background region suppression module based on color layer mask supervision and a key region selection module based on a transformer due to its global modeling capability. Additionally, an unsupervised and unpaired cytoplasm generator network is first proposed to expand the long-tailed distribution dataset. Finally, experiments are performed on BC multicenter datasets. The proposed BC fine detection method outperforms other benchmarks in almost all evaluation criteria, providing clarification and support for tasks such as cancer screenings.Copyright © 2023 Elsevier B.V. All rights reserved.