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

arXiv: https://arxiv.org/abs/1807.09607 · HTML: https://mlsanity.com/paper/1807.09607/

## Key facts

- Cited by 14 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 25 Jul 2018.

## Built on by (most cited first, 14 of 14)

- [A Morphology Focused Diffusion Probabilistic Model for Synthesis of Histopathology Images](https://mlsanity.com/paper/2209.13167/) (2022)
- [Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides](https://mlsanity.com/paper/2112.02222/) (2021)
- [Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysis](https://mlsanity.com/paper/2208.08789/) (2022)
- [FastSurferVINN: Building Resolution-Independence into Deep Learning Segmentation Methods -- A Solution for HighRes Brain MRI](https://mlsanity.com/paper/2112.09654/) (2021)
- [WSI-SAM: Multi-resolution Segment Anything Model (SAM) for histopathology whole-slide images](https://mlsanity.com/paper/2403.09257/) (2024)
- [A Dual-branch Self-supervised Representation Learning Framework for Tumour Segmentation in Whole Slide Images](https://mlsanity.com/paper/2303.11019/) (2023)
- [Valuing Vicinity: Memory attention framework for context-based semantic segmentation in histopathology](https://mlsanity.com/paper/2210.11822/) (2022)
- [Improved distinct bone segmentation in upper-body CT through multi-resolution networks](https://mlsanity.com/paper/2301.13674/) (2023)
- [AtlasPatch: Scalable Foundation Model-based Tissue Detection and Patch Extraction for Computational Pathology](https://mlsanity.com/paper/2602.03998/) (2026)
- [Multi-Stain Multi-Level Convolutional Network for Multi-Tissue Breast Cancer Image Segmentation](https://mlsanity.com/paper/2406.05828/) (2024)
- [Distilling High Diagnostic Value Patches for Whole Slide Image Classification Using Attention Mechanism](https://mlsanity.com/paper/2407.19821/) (2024)
- [TUMLS: Trustful Fully Unsupervised Multi-Level Segmentation for Whole Slide Images of Histology](https://mlsanity.com/paper/2504.12718/) (2025)
- [GNCAF: A GNN-based Neighboring Context Aggregation Framework for Tertiary Lymphoid Structures Semantic Segmentation in WSI](https://mlsanity.com/paper/2505.08430/) (2025)
- [Adapting Vision Transformers to Ultra-High Resolution Semantic Segmentation with Relay Tokens](https://mlsanity.com/paper/2601.05927/) (2026)

## Abstract (arXiv)

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.