CGAM: Click-Guided Attention Module for Interactive Pathology Image Segmentation via Backpropagating Refinement

TL;DR

This paper introduces CGAM, a click-guided attention module for interactive pathology image segmentation, enabling user refinement of deep learning outputs with improved accuracy and stability, suitable for medical applications.

Contribution

The paper presents a novel click-guided attention module that enhances interactive segmentation by effectively integrating user inputs without overfitting, and maintains model size independent of input image dimensions.

Findings

Outperforms existing state-of-the-art methods on pathology datasets.

Reduces overfitting by avoiding excessive changes in segmentation.

Model size remains constant regardless of input image size.

Abstract

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.