# A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends

arXiv: https://arxiv.org/abs/2311.18373 · HTML: https://mlsanity.com/paper/2311.18373/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 66 of its 133 references that are in the map.
- First posted on arXiv 30 Nov 2023.

## Builds on (most cited first, 30 of 66)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://mlsanity.com/paper/2106.13797/) (2021)
- [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)
- [FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space](https://mlsanity.com/paper/2103.06030/) (2021)
- [TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation](https://mlsanity.com/paper/2102.08005/) (2021)
- [A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images](https://mlsanity.com/paper/1612.00799/) (2016)
- [Concealed Object Detection](https://mlsanity.com/paper/2102.10274/) (2021)
- [ResUNet++: An Advanced Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1911.07067/) (2019)
- [Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers](https://mlsanity.com/paper/2108.06932/) (2021)
- [Salient Object Detection: A Benchmark](https://mlsanity.com/paper/1501.02741/) (2015)
- [Shallow Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2108.00882/) (2021)
- [Context-aware Cross-level Fusion Network for Camouflaged Object Detection](https://mlsanity.com/paper/2105.12555/) (2021)
- [Video Polyp Segmentation: A Deep Learning Perspective](https://mlsanity.com/paper/2203.14291/) (2022)
- [Automatic Polyp Segmentation via Multi-scale Subtraction Network](https://mlsanity.com/paper/2108.05082/) (2021)
- [Progressively Normalized Self-Attention Network for Video Polyp Segmentation](https://mlsanity.com/paper/2105.08468/) (2021)
- [Semantic Segmentation using Vision Transformers: A survey](https://mlsanity.com/paper/2305.03273/) (2023)
- [HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS](https://mlsanity.com/paper/2101.07172/) (2021)
- [Polyp-SAM: Transfer SAM for Polyp Segmentation](https://mlsanity.com/paper/2305.00293/) (2023)
- [A multi-centre polyp detection and segmentation dataset for generalisability assessment](https://mlsanity.com/paper/2106.04463/) (2021)
- [UACANet: Uncertainty Augmented Context Attention for Polyp Segmentation](https://mlsanity.com/paper/2107.02368/) (2021)
- [TGANet: Text-guided attention for improved polyp segmentation](https://mlsanity.com/paper/2205.04280/) (2022)
- [Can SAM Segment Polyps?](https://mlsanity.com/paper/2304.07583/) (2023)

## Built on by (most cited first, 12 of 12)

- [Frontiers in Intelligent Colonoscopy](https://mlsanity.com/paper/2410.17241/) (2024)
- [Colorectal Polyp Segmentation in the Deep Learning Era: A Comprehensive Survey](https://mlsanity.com/paper/2401.11734/) (2024)
- [Uncertainty-Masked Bernoulli Diffusion for Camouflaged Object Detection Refinement](https://mlsanity.com/paper/2506.10712/) (2025)
- [Deep Bayesian segmentation for colon polyps: Well-calibrated predictions in medical imaging](https://mlsanity.com/paper/2407.16608/) (2024)
- [MEGANet-W: A Wavelet-Driven Edge-Guided Attention Framework for Weak Boundary Polyp Detection](https://mlsanity.com/paper/2507.02668/) (2025)
- [Cycle Context Verification for In-Context Medical Image Segmentation](https://mlsanity.com/paper/2507.08357/) (2025)
- [MicroAUNet: Boundary-Enhanced Multi-scale Fusion with Knowledge Distillation for Colonoscopy Polyp Image Segmentation](https://mlsanity.com/paper/2511.01143/) (2025)
- [Unlocking Generalization in Polyp Segmentation with DINO Self-Attention "keys"](https://mlsanity.com/paper/2512.13376/) (2025)
- [Adversarial Attacks on Medical Hyperspectral Imaging Exploiting Spectral-Spatial Dependencies and Multiscale Features](https://mlsanity.com/paper/2601.07056/) (2026)
- [SemiGDA: Generative Dual-distribution Alignment for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2604.23274/) (2026)
- [DepthPolyp: Pseudo-Depth Guided Lightweight Segmentation for Real-Time Colonoscopy](https://mlsanity.com/paper/2605.16519/) (2026)
- [Metrics or Mirage? An Audit of Evaluation Inconsistencies in Colonoscopy Polyp Segmentation Benchmarks](https://mlsanity.com/paper/2607.08203/) (2026)

## Abstract (arXiv)

Early detection and assessment of polyps play a crucial role in the prevention and treatment of colorectal cancer (CRC). Polyp segmentation provides an effective solution to assist clinicians in accurately locating and segmenting polyp regions. In the past, people often relied on manually extracted lower-level features such as color, texture, and shape, which often had issues capturing global context and lacked robustness to complex scenarios. With the advent of deep learning, more and more outstanding medical image segmentation algorithms based on deep learning networks have emerged, making significant progress in this field. This paper provides a comprehensive review of polyp segmentation algorithms. We first review some traditional algorithms based on manually extracted features and deep segmentation algorithms, then detail benchmark datasets related to the topic. Specifically, we carry out a comprehensive evaluation of recent deep learning models and results based on polyp sizes, considering the pain points of research topics and differences in network structures. Finally, we discuss the challenges of polyp segmentation and future trends in this field. The models, benchmark datasets, and source code links we collected are all published at https://github.com/taozh2017/Awesome-Polyp-Segmentation.