CGAM: Click-Guided Attention Module for Interactive Pathology Image
  Segmentation via Backpropagating Refinement
Seonghui Min Won-Ki Jeong
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.