# HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images

arXiv: https://arxiv.org/abs/2006.12230 · HTML: https://mlsanity.com/paper/2006.12230/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2020.

## Built on by (most cited first, 30 of 45)

- [Multi-Layer Pseudo-Supervision for Histopathology Tissue Semantic Segmentation using Patch-level Classification Labels](https://mlsanity.com/paper/2110.08048/) (2021)
- [WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma](https://mlsanity.com/paper/2204.06455/) (2022)
- [Global-Local Transformer for Brain Age Estimation](https://mlsanity.com/paper/2109.01663/) (2021)
- [OCELOT: Overlapped Cell on Tissue Dataset for Histopathology](https://mlsanity.com/paper/2303.13110/) (2023)
- [FastSurferVINN: Building Resolution-Independence into Deep Learning Segmentation Methods -- A Solution for HighRes Brain MRI](https://mlsanity.com/paper/2112.09654/) (2021)
- [WaveMix: A Resource-efficient Neural Network for Image Analysis](https://mlsanity.com/paper/2205.14375/) (2022)
- [SDR-Former: A Siamese Dual-Resolution Transformer for Liver Lesion Classification Using 3D Multi-Phase Imaging](https://mlsanity.com/paper/2402.17246/) (2024)
- [RadFormer: Transformers with Global-Local Attention for Interpretable and Accurate Gallbladder Cancer Detection](https://mlsanity.com/paper/2211.04793/) (2022)
- [Segmentation Ability Map: Interpret deep features for medical image segmentation](https://mlsanity.com/paper/2212.09206/) (2022)
- [DPA-P2PNet: Deformable Proposal-aware P2PNet for Accurate Point-based Cell Detection](https://mlsanity.com/paper/2303.02602/) (2023)
- [PDBL: Improving Histopathological Tissue Classification with Plug-and-Play Pyramidal Deep-Broad Learning](https://mlsanity.com/paper/2111.03063/) (2021)
- [AMD-HookNet for Glacier Front Segmentation](https://mlsanity.com/paper/2302.02744/) (2023)
- [WSI-SAM: Multi-resolution Segment Anything Model (SAM) for histopathology whole-slide images](https://mlsanity.com/paper/2403.09257/) (2024)
- [Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation](https://mlsanity.com/paper/2504.18856/) (2025)
- [A Dual-branch Self-supervised Representation Learning Framework for Tumour Segmentation in Whole Slide Images](https://mlsanity.com/paper/2303.11019/) (2023)
- [Towards Effective and Efficient Context-aware Nucleus Detection in Histopathology Whole Slide Images](https://mlsanity.com/paper/2503.05678/) (2025)
- [BROW: Better featuRes fOr Whole slide image based on self-distillation](https://mlsanity.com/paper/2309.08259/) (2023)
- [A Multi-Stage Auto-Context Deep Learning Framework for Tissue and Nuclei Segmentation and Classification in H&E-Stained Histological Images of Advanced Melanoma](https://mlsanity.com/paper/2503.23958/) (2025)
- [Valuing Vicinity: Memory attention framework for context-based semantic segmentation in histopathology](https://mlsanity.com/paper/2210.11822/) (2022)
- [Comparison Study: Glacier Calving Front Delineation in Synthetic Aperture Radar Images With Deep Learning](https://mlsanity.com/paper/2501.05281/) (2025)
- [PathoPainter: Augmenting Histopathology Segmentation via Tumor-aware Inpainting](https://mlsanity.com/paper/2503.04634/) (2025)
- [Co-Seg++: Mutual Prompt-Guided Collaborative Learning for Versatile Medical Segmentation](https://mlsanity.com/paper/2506.17159/) (2025)
- [CD-Net: Histopathology Representation Learning using Pyramidal Context-Detail Network](https://mlsanity.com/paper/2203.15078/) (2022)
- [Improved distinct bone segmentation in upper-body CT through multi-resolution networks](https://mlsanity.com/paper/2301.13674/) (2023)
- [Increasing the usefulness of already existing annotations through WSI registration](https://mlsanity.com/paper/2303.06727/) (2023)
- [Histo-Miner: Deep learning based tissue features extraction pipeline from H&E whole slide images of cutaneous squamous cell carcinoma](https://mlsanity.com/paper/2505.04672/) (2025)
- [A Multicentric Dataset for Training and Benchmarking Breast Cancer Segmentation in H&E Slides](https://mlsanity.com/paper/2510.02037/) (2025)
- [Automatic tumour segmentation in H&E-stained whole-slide images of the pancreas](https://mlsanity.com/paper/2112.01533/) (2021)
- [Integrating multiscale topology in digital pathology with pyramidal graph convolutional networks](https://mlsanity.com/paper/2403.15068/) (2024)
- [AMD-HookNet++: Evolution of AMD-HookNet with Hybrid CNN-Transformer Feature Enhancement for Glacier Calving Front Segmentation](https://mlsanity.com/paper/2512.14639/) (2025)

## Abstract (arXiv)

We propose HookNet, a semantic segmentation model for histopathology whole-slide images, which combines context and details via multiple branches of encoder-decoder convolutional neural networks. Concentricpatches at multiple resolutions with different fields of view are used to feed different branches of HookNet, and intermediate representations are combined via a hooking mechanism. We describe a framework to design and train HookNet for achieving high-resolution semantic segmentation and introduce constraints to guarantee pixel-wise alignment in feature maps during hooking. We show the advantages of using HookNet in two histopathology image segmentation tasks where tissue type prediction accuracy strongly depends on contextual information, namely (1) multi-class tissue segmentation in breast cancer and, (2) segmentation of tertiary lymphoid structures and germinal centers in lung cancer. Weshow the superiority of HookNet when compared with single-resolution U-Net models working at different resolutions as well as with a recently published multi-resolution model for histopathology image segmentation