# GMSRF-Net: An improved generalizability with global multi-scale residual fusion network for polyp segmentation

arXiv: https://arxiv.org/abs/2111.10614 · HTML: https://mlsanity.com/paper/2111.10614/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 24 references that are in the map.
- First posted on arXiv 20 Nov 2021.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [PraNet: Parallel Reverse Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2006.11392/) (2020)
- [ResUNet++: An Advanced Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1911.07067/) (2019)
- [MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation](https://mlsanity.com/paper/1902.04049/) (2019)
- [Receptive Field Block Net for Accurate and Fast Object Detection](https://mlsanity.com/paper/1711.07767/) (2017)
- [UACANet: Uncertainty Augmented Context Attention for Polyp Segmentation](https://mlsanity.com/paper/2107.02368/) (2021)
- [MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation](https://mlsanity.com/paper/2105.07451/) (2021)
- [IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks](https://mlsanity.com/paper/1806.00178/) (2018)
- [Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning](https://mlsanity.com/paper/2011.07631/) (2020)
- [IGCV2: Interleaved Structured Sparse Convolutional Neural Networks](https://mlsanity.com/paper/1804.06202/) (2018)

## Built on by (most cited first, 11 of 11)

- [A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends](https://mlsanity.com/paper/2311.18373/) (2023)
- [Video Capsule Endoscopy Classification using Focal Modulation Guided Convolutional Neural Network](https://mlsanity.com/paper/2206.08298/) (2022)
- [Does DINOv3 Set a New Medical Vision Standard? Benchmarking 2D and 3D Classification, Segmentation, and Registration](https://mlsanity.com/paper/2509.06467/) (2025)
- [An Efficient Multi-Scale Fusion Network for 3D Organ at Risk (OAR) Segmentation](https://mlsanity.com/paper/2208.07417/) (2022)
- [Multi-scale Information Sharing and Selection Network with Boundary Attention for Polyp Segmentation](https://mlsanity.com/paper/2405.11151/) (2024)
- [LAPFormer: A Light and Accurate Polyp Segmentation Transformer](https://mlsanity.com/paper/2210.04393/) (2022)
- [Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation](https://mlsanity.com/paper/2210.16704/) (2022)
- [DSNet: a simple yet efficient network with dual-stream attention for lesion segmentation](https://mlsanity.com/paper/2211.16950/) (2022)
- [RUPNet: Residual upsampling network for real-time polyp segmentation](https://mlsanity.com/paper/2301.02703/) (2023)
- [Polyp segmentation in colonoscopy images using DeepLabV3++](https://mlsanity.com/paper/2407.19327/) (2024)
- [FocusNet: Transformer-enhanced Polyp Segmentation with Local and Pooling Attention](https://mlsanity.com/paper/2504.13597/) (2025)

## Abstract (arXiv)

Colonoscopy is a gold standard procedure but is highly operator-dependent. Efforts have been made to automate the detection and segmentation of polyps, a precancerous precursor, to effectively minimize missed rate. Widely used computer-aided polyp segmentation systems actuated by encoder-decoder have achieved high performance in terms of accuracy. However, polyp segmentation datasets collected from varied centers can follow different imaging protocols leading to difference in data distribution. As a result, most methods suffer from performance drop and require re-training for each specific dataset. We address this generalizability issue by proposing a global multi-scale residual fusion network (GMSRF-Net). Our proposed network maintains high-resolution representations while performing multi-scale fusion operations for all resolution scales. To further leverage scale information, we design cross multi-scale attention (CMSA) and multi-scale feature selection (MSFS) modules within the GMSRF-Net. The repeated fusion operations gated by CMSA and MSFS demonstrate improved generalizability of the network. Experiments conducted on two different polyp segmentation datasets show that our proposed GMSRF-Net outperforms the previous top-performing state-of-the-art method by 8.34% and 10.31% on unseen CVC-ClinicDB and unseen Kvasir-SEG, in terms of dice coefficient.