# Multi-Resolution Networks for Semantic Segmentation in Whole Slide Images

Source: [Multi-Resolution Networks for Semantic Segmentation in Whole Slide Images](<https://www.sota2.com/research/paper/68fa649b1e209131cea0580a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:08:02.971Z

Authors: Feng Gu, Nikolay Burlutskiy, Mats Andersson, Lena Kajland Wilen

Published: 2018-07-25T13:54:11.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1807.09607>)

[PDF](<https://arxiv.org/pdf/1807.09607.pdf>)