CGAM：基于反向传播精修的交互式病理图像分割点击引导注意力模块
CGAM: Click-Guided Attention Module for Interactive Pathology Image Segmentation via Backpropagating Refinement
AI总结：
本文提出一种交互式病理图像分割方法，通过点击引导注意力模块（CGAM）将交互式分割转化为优化问题，结合用户点击约束与特征图语义信息精修网络输出，避免对用户点击过拟合且模型大小与输入无关，性能优于现有最先进方法。
摘要：
肿瘤区域分割是数字病理学定量分析的一项重要任务。最近提出的深度神经网络在各种图像分割任务中展现出了最先进的性能。然而，由于病理图像中癌变区域与正常区域之间的边界不清晰，尽管采用了现代方法，在医学数据所需的可靠性和准确性方面，仍难以产生令人满意的分割结果。在本研究中，我们提出了一种交互式分割方法，允许用户通过点击类型的用户交互来精修深度神经网络的输出。主要方法是将交互式分割表述为一个优化问题，利用点击引导注意力模块（CGAM）同时结合用户提供的点击约束和特征图中的语义信息。与现有的其他方法不同，CGAM避免了分割结果的过度变化，这种过度变化可能导致对用户点击的过拟合。CGAM的另一个优点是模型大小与输入图像大小无关。在病理图像数据集上的实验结果表明，我们的方法优于现有的最先进方法。
英文摘要：
Tumor region segmentation is an essential task for the quantitative analysis of digital pathology. Recently presented deep neural networks have shown state-of-the-art performance in various image-segmentation tasks. However, because of the unclear boundary between the cancerous and normal regions in pathology images, despite using modern methods, it is difficult to produce satisfactory segmentation results in terms of the reliability and accuracy required for medical data. In this study, we propose an interactive segmentation method that allows users to refine the output of deep neural networks through click-type user interactions. The primary method is to formulate interactive segmentation as an optimization problem that leverages both user-provided click constraints and semantic information in a feature map using a click-guided attention module (CGAM). Unlike other existing methods, CGAM avoids excessive changes in segmentation results, which can lead to the overfitting of user clicks. Another advantage of CGAM is that the model size is independent of input image size. Experimental results on pathology image datasets indicated that our method performs better than existing state-of-the-art methods.
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。
