Multi-Resolution Networks for Semantic Segmentation in Whole Slide Images

Autor:	Gu, Feng, Burlutskiy, Nikolay, Andersson, Mats, Wilen, Lena Kajland
Rok vydání:	2018
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted by MICCAI COMPAY 2018 Workshop
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1807.09607 Zobrazit plný text záznamu View this record from Arxiv