Multi-Resolution Networks for Semantic Segmentation in Whole Slide Images
This work addresses a domain-specific problem in digital pathology for improved medical image analysis, but it is incremental as it builds on existing U-Net architecture.
The paper tackled the challenge of multi-resolution in semantic segmentation of whole slide images by proposing two novel networks based on U-Net, which outperformed U-Net on a benchmark dataset, showing superior learning and generalization capabilities.
Digital pathology provides an excellent opportunity for applying fully convolutional networks (FCNs) to tasks, such as semantic segmentation of whole slide images (WSIs). However, standard FCNs face challenges with respect to multi-resolution, inherited from the pyramid arrangement of WSIs. As a result, networks specifically designed to learn and aggregate information at different levels are desired. In this paper, we propose two novel multi-resolution networks based on the popular `U-Net' architecture, which are evaluated on a benchmark dataset for binary semantic segmentation in WSIs. The proposed methods outperform the U-Net, demonstrating superior learning and generalization capabilities.