利用弱监督进行数字病理学中细胞定位的多任务学习与一致性损失
Leveraging Weak Supervision for Cell Localization in Digital Pathology Using Multitask Learning and Consistency Loss
AI总结：
本研究提出一种用于数字病理学的混合监督多任务网络，首次将病理学家目测估计的细胞计数作为辅助监督信号，通过一致性损失联合学习细胞计数与定位，有效利用弱标注提升性能。
摘要：
细胞检测和分割是数字病理学自动化系统的组成部分。编码器-解码器网络已成为这些任务的有前途的解决方案。然而，这些网络的训练通常需要细胞的完整边界标注，这非常耗费人力且难以大规模获取。然而，在许多应用中，例如细胞计数，较弱的标注形式——如点标注或近似细胞计数——可以为训练提供足够的监督。本研究提出了一种新的混合监督方法，通过纳入从目测过程中得出的细胞计数——一种病理学家常用的快速视觉估计方法——来训练数字病理学中的多任务网络。本研究有两个主要贡献：(1) 首次提出了一种数字病理学的混合监督策略，利用通过目测获得的细胞计数作为辅助监督信号来训练多任务网络。(2) 该多任务网络被设计为同时学习细胞计数和细胞定位任务，并且本研究引入了一种一致性损失，通过惩罚这两个任务预测之间的不一致性来正则化训练。我们在两个苏木精-伊红染色组织图像数据集上的实验表明，所提出的方法有效地利用了最弱形式的标注，在强标注有限时提高了性能。这些结果突出了将目测得出的真实标签整合到网络训练中的潜力，减少了对资源密集型标注的需求。
英文摘要：
Cell detection and segmentation are integral parts of automated systems in digital pathology. Encoder-decoder networks have emerged as a promising solution for these tasks. However, training of these networks has typically required full boundary annotations of cells, which are labor-intensive and difficult to obtain on a large scale. However, in many applications, such as cell counting, weaker forms of annotations--such as point annotations or approximate cell counts--can provide sufficient supervision for training. This study proposes a new mixed-supervision approach for training multitask networks in digital pathology by incorporating cell counts derived from the eyeballing process--a quick visual estimation method commonly used by pathologists. This study has two main contributions: (1) It proposes a mixed-supervision strategy for digital pathology that utilizes cell counts obtained by eyeballing as an auxiliary supervisory signal to train a multitask network for the first time. (2) This multitask network is designed to concurrently learn the tasks of cell counting and cell localization, and this study introduces a consistency loss that regularizes training by penalizing inconsistencies between the predictions of these two tasks. Our experiments on two datasets of hematoxylin-eosin stained tissue images demonstrate that the proposed approach effectively utilizes the weakest form of annotation, improving performance when stronger annotations are limited. These results highlight the potential of integrating eyeballing-derived ground truths into the network training, reducing the need for resource-intensive annotations.
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。
