论文标题
免疫组织化学细胞质染色图像中细胞识别的细胞识别的弱监督学习图像
Weakly Supervised Learning for cell recognition in immunohistochemical cytoplasm staining images
论文作者
论文摘要
免疫组织化学细胞质染色图像中的细胞分类和计数在癌症诊断中起关键作用。弱监督的学习是处理劳动密集型标签的潜在方法。但是，班级之间的不变细胞形态和微妙的差异也带来了挑战。为此，我们提出了一个基于多任务学习的新型细胞识别框架，该框架利用了另外两个辅助任务来指导对主要任务的强大表示学习。为了处理错误分类，引入了组织先进的学习分支，以捕获没有其他组织注释的肿瘤细胞的空间表示。此外，采用动态面具和一致性学习来学习细胞尺度和形状的不变性。我们已经评估了有关免疫组织化学细胞质染色图像的框架，结果表明，我们的方法表现优于最近的细胞识别方法。此外，我们还进行了一些消融研究，以显示添加辅助分支后的显着改善。
Cell classification and counting in immunohistochemical cytoplasm staining images play a pivotal role in cancer diagnosis. Weakly supervised learning is a potential method to deal with labor-intensive labeling. However, the inconstant cell morphology and subtle differences between classes also bring challenges. To this end, we present a novel cell recognition framework based on multi-task learning, which utilizes two additional auxiliary tasks to guide robust representation learning of the main task. To deal with misclassification, the tissue prior learning branch is introduced to capture the spatial representation of tumor cells without additional tissue annotation. Moreover, dynamic masks and consistency learning are adopted to learn the invariance of cell scale and shape. We have evaluated our framework on immunohistochemical cytoplasm staining images, and the results demonstrate that our method outperforms recent cell recognition approaches. Besides, we have also done some ablation studies to show significant improvements after adding the auxiliary branches.
