# Automatic Polyp Segmentation via Multi-scale Subtraction Network

arXiv: https://arxiv.org/abs/2108.05082 · HTML: https://mlsanity.com/paper/2108.05082/

## Key facts

- Cited by 77 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 11 Aug 2021.

## Builds on (most cited first, 17 of 17)

- [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)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [Structure-measure: A New Way to Evaluate Foreground Maps](https://mlsanity.com/paper/1708.00786/) (2017)
- [PraNet: Parallel Reverse Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2006.11392/) (2020)
- [Enhanced-alignment Measure for Binary Foreground Map Evaluation](https://mlsanity.com/paper/1805.10421/) (2018)
- [A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images](https://mlsanity.com/paper/1612.00799/) (2016)
- [ResUNet++: An Advanced Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1911.07067/) (2019)
- [F3Net: Fusion, Feedback and Focus for Salient Object Detection](https://mlsanity.com/paper/1911.11445/) (2019)
- [Progressively Normalized Self-Attention Network for Video Polyp Segmentation](https://mlsanity.com/paper/2105.08468/) (2021)
- [Polyp Segmentation in Colonoscopy Images Using Fully Convolutional Network](https://mlsanity.com/paper/1802.00368/) (2018)

## Built on by (most cited first, 30 of 77)

- [Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers](https://mlsanity.com/paper/2108.06932/) (2021)
- [Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection](https://mlsanity.com/paper/2203.02688/) (2022)
- [Video Polyp Segmentation: A Deep Learning Perspective](https://mlsanity.com/paper/2203.14291/) (2022)
- [Deep Gradient Learning for Efficient Camouflaged Object Detection](https://mlsanity.com/paper/2205.12853/) (2022)
- [Can SAM Segment Polyps?](https://mlsanity.com/paper/2304.07583/) (2023)
- [CAVER: Cross-Modal View-Mixed Transformer for Bi-Modal Salient Object Detection](https://mlsanity.com/paper/2112.02363/) (2021)
- [H-vmunet: High-order Vision Mamba UNet for Medical Image Segmentation](https://mlsanity.com/paper/2403.13642/) (2024)
- [MEGANet: Multi-Scale Edge-Guided Attention Network for Weak Boundary Polyp Segmentation](https://mlsanity.com/paper/2309.03329/) (2023)
- [Modality-agnostic Domain Generalizable Medical Image Segmentation by Multi-Frequency in Multi-Scale Attention](https://mlsanity.com/paper/2405.06284/) (2024)
- [Polyp-SAM++: Can A Text Guided SAM Perform Better for Polyp Segmentation?](https://mlsanity.com/paper/2308.06623/) (2023)
- [SAM-COD: SAM-guided Unified Framework for Weakly-Supervised Camouflaged Object Detection](https://mlsanity.com/paper/2408.10760/) (2024)
- [WeakPolyp: You Only Look Bounding Box for Polyp Segmentation](https://mlsanity.com/paper/2307.10912/) (2023)
- [Spider: A Unified Framework for Context-dependent Concept Segmentation](https://mlsanity.com/paper/2405.01002/) (2024)
- [Towards Diverse Binary Segmentation via A Simple yet General Gated Network](https://mlsanity.com/paper/2303.10396/) (2023)
- [Frontiers in Intelligent Colonoscopy](https://mlsanity.com/paper/2410.17241/) (2024)
- [M2SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation](https://mlsanity.com/paper/2303.10894/) (2023)
- [Joint Learning of Salient Object Detection, Depth Estimation and Contour Extraction](https://mlsanity.com/paper/2203.04895/) (2022)
- [Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection](https://mlsanity.com/paper/2205.07179/) (2022)
- [A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends](https://mlsanity.com/paper/2311.18373/) (2023)
- [ASPS: Augmented Segment Anything Model for Polyp Segmentation](https://mlsanity.com/paper/2407.00718/) (2024)
- [BoxPolyp:Boost Generalized Polyp Segmentation Using Extra Coarse Bounding Box Annotations](https://mlsanity.com/paper/2212.03498/) (2022)
- [Concealed Object Segmentation with Hierarchical Coherence Modeling](https://mlsanity.com/paper/2401.11767/) (2024)
- [Inspiring the Next Generation of Segment Anything Models: Comprehensively Evaluate SAM and SAM 2 with Diverse Prompts Towards Context-Dependent Concepts under Different Scenes](https://mlsanity.com/paper/2412.01240/) (2024)
- [Polyp SAM 2: Advancing Zero shot Polyp Segmentation in Colorectal Cancer Detection](https://mlsanity.com/paper/2408.05892/) (2024)
- [SAM-Mamba: Mamba Guided SAM Architecture for Generalized Zero-Shot Polyp Segmentation](https://mlsanity.com/paper/2412.08482/) (2024)
- [CGCOD: Class-Guided Camouflaged Object Detection](https://mlsanity.com/paper/2412.18977/) (2024)
- [TP-Seg: Task-Prototype Framework for Unified Medical Lesion Segmentation](https://mlsanity.com/paper/2604.00684/) (2026)
- [DilatedSegNet: A Deep Dilated Segmentation Network for Polyp Segmentation](https://mlsanity.com/paper/2210.13595/) (2022)
- [Meta-Polyp: a baseline for efficient Polyp segmentation](https://mlsanity.com/paper/2305.07848/) (2023)
- [Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model](https://mlsanity.com/paper/2409.09484/) (2024)

## Abstract (arXiv)

More than 90% of colorectal cancer is gradually transformed from colorectal polyps. In clinical practice, precise polyp segmentation provides important information in the early detection of colorectal cancer. Therefore, automatic polyp segmentation techniques are of great importance for both patients and doctors. Most existing methods are based on U-shape structure and use element-wise addition or concatenation to fuse different level features progressively in decoder. However, both the two operations easily generate plenty of redundant information, which will weaken the complementarity between different level features, resulting in inaccurate localization and blurred edges of polyps. To address this challenge, we propose a multi-scale subtraction network (MSNet) to segment polyp from colonoscopy image. Specifically, we first design a subtraction unit (SU) to produce the difference features between adjacent levels in encoder. Then, we pyramidally equip the SUs at different levels with varying receptive fields, thereby obtaining rich multi-scale difference information. In addition, we build a training-free network "LossNet" to comprehensively supervise the polyp-aware features from bottom layer to top layer, which drives the MSNet to capture the detailed and structural cues simultaneously. Extensive experiments on five benchmark datasets demonstrate that our MSNet performs favorably against most state-of-the-art methods under different evaluation metrics. Furthermore, MSNet runs at a real-time speed of ~70fps when processing a 352 × 352 image. The source code will be publicly available at urlhttps://github.com/Xiaoqi-Zhao-DLUT/MSNet. keywordsColorectal Cancer and Automatic Polyp Segmentation and Subtraction and LossNet.