CGAM: Click-Guided Attention Module for Interactive Pathology Image Segmentation via Backpropagating Refinement
This addresses the challenge of unclear boundaries in pathology images for medical professionals, offering an incremental improvement over existing interactive segmentation techniques.
The authors tackled the problem of unreliable tumor segmentation in pathology images by proposing an interactive method that refines deep neural network outputs using user clicks, achieving better performance than existing state-of-the-art methods.
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.