# Polyper: Boundary Sensitive Polyp Segmentation

arXiv: https://arxiv.org/abs/2312.08735 · HTML: https://mlsanity.com/paper/2312.08735/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 43 references that are in the map.
- First posted on arXiv 14 Dec 2023.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer](https://mlsanity.com/paper/1612.03928/) (2016)
- [PraNet: Parallel Reverse Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2006.11392/) (2020)
- [Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers](https://mlsanity.com/paper/2108.06932/) (2021)
- [Reverse Attention for Salient Object Detection](https://mlsanity.com/paper/1807.09940/) (2018)
- [Shallow Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2108.00882/) (2021)
- [Segmenting Transparent Objects in the Wild](https://mlsanity.com/paper/2003.13948/) (2020)
- [Is Attention Better Than Matrix Decomposition?](https://mlsanity.com/paper/2109.04553/) (2021)
- [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)
- [CamoFormer: Masked Separable Attention for Camouflaged Object Detection](https://mlsanity.com/paper/2212.06570/) (2022)
- [Medical Image Segmentation Using Squeeze-and-Expansion Transformers](https://mlsanity.com/paper/2105.09511/) (2021)
- [KiU-Net: Towards Accurate Segmentation of Biomedical Images using Over-complete Representations](https://mlsanity.com/paper/2006.04878/) (2020)
- [Enhanced Boundary Learning for Glass-like Object Segmentation](https://mlsanity.com/paper/2103.15734/) (2021)
- [Adaptive Context Selection for Polyp Segmentation](https://mlsanity.com/paper/2301.04799/) (2023)
- [CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objects](https://mlsanity.com/paper/2301.13366/) (2023)
- [ColonFormer: An Efficient Transformer based Method for Colon Polyp Segmentation](https://mlsanity.com/paper/2205.08473/) (2022)
- [RFENet: Towards Reciprocal Feature Evolution for Glass Segmentation](https://mlsanity.com/paper/2307.06099/) (2023)
- [RTNet: Relation Transformer Network for Diabetic Retinopathy Multi-lesion Segmentation](https://mlsanity.com/paper/2201.11037/) (2022)
- [Edge-Aware Mirror Network for Camouflaged Object Detection](https://mlsanity.com/paper/2307.03932/) (2023)
- [AttResDU-Net: Medical Image Segmentation Using Attention-based Residual Double U-Net](https://mlsanity.com/paper/2306.14255/) (2023)

## Built on by (most cited first, 8 of 8)

- [Frontiers in Intelligent Colonoscopy](https://mlsanity.com/paper/2410.17241/) (2024)
- [PraNet-V2: Dual-Supervised Reverse Attention for Medical Image Segmentation](https://mlsanity.com/paper/2504.10986/) (2025)
- [ASGNet: Adaptive Spectrum Guidance Network for Automatic Polyp Segmentation](https://mlsanity.com/paper/2604.14755/) (2026)
- [MedCore: Boundary-Preserving Medical Core Pruning for MedSAM](https://mlsanity.com/paper/2605.13688/) (2026)
- [MEGANet-W: A Wavelet-Driven Edge-Guided Attention Framework for Weak Boundary Polyp Detection](https://mlsanity.com/paper/2507.02668/) (2025)
- [FreeVPS: Repurposing Training-Free SAM2 for Generalizable Video Polyp Segmentation](https://mlsanity.com/paper/2508.19705/) (2025)
- [Polyp Segmentation Using Wavelet-Based Cross-Band Integration for Enhanced Boundary Representation](https://mlsanity.com/paper/2603.03682/) (2026)
- [APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms](https://mlsanity.com/paper/2606.30577/) (2026)

## Abstract (arXiv)

We present a new boundary sensitive framework for polyp segmentation, called Polyper. Our method is motivated by a clinical approach that seasoned medical practitioners often leverage the inherent features of interior polyp regions to tackle blurred boundaries.Inspired by this, we propose explicitly leveraging polyp regions to bolster the model's boundary discrimination capability while minimizing computation. Our approach first extracts boundary and polyp regions from the initial segmentation map through morphological operators. Then, we design the boundary sensitive attention that concentrates on augmenting the features near the boundary regions using the interior polyp regions's characteristics to generate good segmentation results. Our proposed method can be seamlessly integrated with classical encoder networks, like ResNet-50, MiT-B1, and Swin Transformer. To evaluate the effectiveness of Polyper, we conduct experiments on five publicly available challenging datasets, and receive state-of-the-art performance on all of them. Code is available at https://github.com/haoshao-nku/medical_seg.git.