Improving Whole Slide Segmentation Through Visual Context - A Systematic Study
This work addresses the need for better segmentation in histology images for medical diagnosis, but it is incremental as it focuses on comparing existing methods rather than introducing a new approach.
The paper tackled the problem of dense segmentation in histology images by systematically comparing architectures that incorporate multi-scale visual context, finding that such context significantly improves performance on breast and prostate cancer datasets.
While challenging, the dense segmentation of histology images is a necessary first step to assess changes in tissue architecture and cellular morphology. Although specific convolutional neural network architectures have been applied with great success to the problem, few effectively incorporate visual context information from multiple scales. With this paper, we present a systematic comparison of different architectures to assess how including multi-scale information affects segmentation performance. A publicly available breast cancer and a locally collected prostate cancer datasets are being utilised for this study. The results support our hypothesis that visual context and scale play a crucial role in histology image classification problems.