# FCB-SwinV2 Transformer for Polyp Segmentation

arXiv: https://arxiv.org/abs/2302.01027 · HTML: https://mlsanity.com/paper/2302.01027/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 2 Feb 2023.

## Builds on (most cited first, 14 of 14)

- [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)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [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)
- [Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks](https://mlsanity.com/paper/1803.02579/) (2018)
- [MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation](https://mlsanity.com/paper/2105.07451/) (2021)
- [FCN-Transformer Feature Fusion for Polyp Segmentation](https://mlsanity.com/paper/2208.08352/) (2022)
- [DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation](https://mlsanity.com/paper/2012.15245/) (2020)
- [ESFPNet: efficient deep learning architecture for real-time lesion segmentation in autofluorescence bronchoscopic video](https://mlsanity.com/paper/2207.07759/) (2022)

## Built on by (most cited first, 6 of 6)

- [Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Models](https://mlsanity.com/paper/2308.07706/) (2023)
- [Meta-Polyp: a baseline for efficient Polyp segmentation](https://mlsanity.com/paper/2305.07848/) (2023)
- [STS MICCAI 2023 Challenge: Grand challenge on 2D and 3D semi-supervised tooth segmentation](https://mlsanity.com/paper/2407.13246/) (2024)
- [Pixel-wise Modulated Dice Loss for Medical Image Segmentation](https://mlsanity.com/paper/2506.15744/) (2025)
- [Ensemble architecture in polyp segmentation](https://mlsanity.com/paper/2408.07262/) (2024)
- [HadBalance: A Plug-and-Play Unified Global Geometric Prior Framework for Generalizable Biomedical Segmentation](https://mlsanity.com/paper/2606.15976/) (2026)

## Abstract (arXiv)

Polyp segmentation within colonoscopy video frames using deep learning models has the potential to automate the workflow of clinicians. This could help improve the early detection rate and characterization of polyps which could progress to colorectal cancer. Recent state-of-the-art deep learning polyp segmentation models have combined the outputs of Fully Convolutional Network architectures and Transformer Network architectures which work in parallel. In this paper we propose modifications to the current state-of-the-art polyp segmentation model FCBFormer. The transformer architecture of the FCBFormer is replaced with a SwinV2 Transformer-UNET and minor changes to the Fully Convolutional Network architecture are made to create the FCB-SwinV2 Transformer. The performance of the FCB-SwinV2 Transformer is evaluated on the popular colonoscopy segmentation bench-marking datasets Kvasir-SEG and CVC-ClinicDB. Generalizability tests are also conducted. The FCB-SwinV2 Transformer is able to consistently achieve higher mDice scores across all tests conducted and therefore represents new state-of-the-art performance. Issues found with how colonoscopy segmentation model performance is evaluated within literature are also re-ported and discussed. One of the most important issues identified is that when evaluating performance on the CVC-ClinicDB dataset it would be preferable to ensure no data leakage from video sequences occurs during the training/validation/test data partition.