# PraNet-V2: Dual-Supervised Reverse Attention for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2504.10986 · HTML: https://mlsanity.com/paper/2504.10986/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 38 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Builds on (most cited first, 29 of 29)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [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)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [Structure-measure: A New Way to Evaluate Foreground Maps](https://mlsanity.com/paper/1708.00786/) (2017)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [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)
- [UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation](https://mlsanity.com/paper/2004.08790/) (2020)
- [A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images](https://mlsanity.com/paper/1612.00799/) (2016)
- [nnFormer: Interleaved Transformer for Volumetric Segmentation](https://mlsanity.com/paper/2109.03201/) (2021)
- [Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers](https://mlsanity.com/paper/2108.06932/) (2021)
- [EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation](https://mlsanity.com/paper/2405.06880/) (2024)
- [UACANet: Uncertainty Augmented Context Attention for Polyp Segmentation](https://mlsanity.com/paper/2107.02368/) (2021)
- [Stepwise Feature Fusion: Local Guides Global](https://mlsanity.com/paper/2203.03635/) (2022)
- [One-Prompt to Segment All Medical Images](https://mlsanity.com/paper/2305.10300/) (2023)
- [Multi-scale Hierarchical Vision Transformer with Cascaded Attention Decoding for Medical Image Segmentation](https://mlsanity.com/paper/2303.16892/) (2023)
- [LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image Segmentation](https://mlsanity.com/paper/2403.07332/) (2024)
- [Open-World Semantic Segmentation Including Class Similarity](https://mlsanity.com/paper/2403.07532/) (2024)
- [SelfReg-UNet: Self-Regularized UNet for Medical Image Segmentation](https://mlsanity.com/paper/2406.14896/) (2024)
- [Frontiers in Intelligent Colonoscopy](https://mlsanity.com/paper/2410.17241/) (2024)
- [MIST: Medical Image Segmentation Transformer with Convolutional Attention Mixing (CAM) Decoder](https://mlsanity.com/paper/2310.19898/) (2023)
- [ASPS: Augmented Segment Anything Model for Polyp Segmentation](https://mlsanity.com/paper/2407.00718/) (2024)
- [Polyper: Boundary Sensitive Polyp Segmentation](https://mlsanity.com/paper/2312.08735/) (2023)

## Built on by (most cited first, 4 of 4)

- [SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos](https://mlsanity.com/paper/2602.05638/) (2026)
- [DiSa: Saliency-Aware Foreground-Background Disentangled Framework for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2601.20064/) (2026)
- [MicroAUNet: Boundary-Enhanced Multi-scale Fusion with Knowledge Distillation for Colonoscopy Polyp Image Segmentation](https://mlsanity.com/paper/2511.01143/) (2025)
- [MedCAGD: Context-Aware Gated Decoder for Efficient Medical Image Segmentation](https://mlsanity.com/paper/2607.00409/) (2026)

## Abstract (arXiv)

Accurate medical image segmentation is essential for effective diagnosis and treatment. Previously, PraNet-V1 was proposed to enhance polyp segmentation by introducing a reverse attention (RA) module that utilizes background information. However, PraNet-V1 struggles with multi-class segmentation tasks. To address this limitation, we propose PraNet-V2, which, compared to PraNet-V1, effectively performs a broader range of tasks including multi-class segmentation. At the core of PraNet-V2 is the Dual-Supervised Reverse Attention (DSRA) module, which incorporates explicit background supervision, independent background modeling, and semantically enriched attention fusion. Our PraNet-V2 framework demonstrates strong performance on four polyp segmentation datasets. Additionally, by integrating DSRA to iteratively enhance foreground segmentation results in three state-of-the-art semantic segmentation models, we achieve up to a 1.36% improvement in mean Dice score. Code is available at: https://github.com/ai4colonoscopy/PraNet-V2/tree/main/binary_seg/jittor.