# TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2207.03450 · HTML: https://mlsanity.com/paper/2207.03450/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 26 references that are in the map.
- First posted on arXiv 7 Jul 2022.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (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)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [Medical Transformer: Gated Axial-Attention for Medical Image Segmentation](https://mlsanity.com/paper/2102.10662/) (2021)
- [DeepViT: Towards Deeper Vision Transformer](https://mlsanity.com/paper/2103.11886/) (2021)
- [Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks](https://mlsanity.com/paper/1807.07356/) (2018)
- [The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation](https://mlsanity.com/paper/1611.09326/) (2016)
- [LViT: Language meets Vision Transformer in Medical Image Segmentation](https://mlsanity.com/paper/2206.14718/) (2022)
- [DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation](https://mlsanity.com/paper/2012.15245/) (2020)
- [CUAB: Convolutional Uncertainty Attention Block Enhanced the Chest X-ray Image Analysis](https://mlsanity.com/paper/2105.01840/) (2021)

## Built on by (most cited first, 18 of 18)

- [ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation](https://mlsanity.com/paper/2402.02029/) (2024)
- [ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embedding](https://mlsanity.com/paper/2307.16226/) (2023)
- [A Recent Survey of Vision Transformers for Medical Image Segmentation](https://mlsanity.com/paper/2312.00634/) (2023)
- [Rethinking Abdominal Organ Segmentation (RAOS) in the clinical scenario: A robustness evaluation benchmark with challenging cases](https://mlsanity.com/paper/2406.13674/) (2024)
- [T-Mamba: A unified framework with Long-Range Dependency in dual-domain for 2D & 3D Tooth Segmentation](https://mlsanity.com/paper/2404.01065/) (2024)
- [MIST: Medical Image Segmentation Transformer with Convolutional Attention Mixing (CAM) Decoder](https://mlsanity.com/paper/2310.19898/) (2023)
- [Recent Progress in Transformer-based Medical Image Analysis](https://mlsanity.com/paper/2208.06643/) (2022)
- [Dilated-UNet: A Fast and Accurate Medical Image Segmentation Approach using a Dilated Transformer and U-Net Architecture](https://mlsanity.com/paper/2304.11450/) (2023)
- [Coarse-to-Fine Covid-19 Segmentation via Vision-Language Alignment](https://mlsanity.com/paper/2303.00279/) (2023)
- [STPNet: Scale-aware Text Prompt Network for Medical Image Segmentation](https://mlsanity.com/paper/2504.01561/) (2025)
- [Automatic Bat Call Classification using Transformer Networks](https://mlsanity.com/paper/2309.11218/) (2023)
- [Medical Image Segmentation via Sparse Coding Decoder](https://mlsanity.com/paper/2310.10957/) (2023)
- [Transfer Learning for Microstructure Segmentation with CS-UNet: A Hybrid Algorithm with Transformer and CNN Encoders](https://mlsanity.com/paper/2308.13917/) (2023)
- [CARE: Anti-entanglement Ultrasound Image Segmentation via Channel-Aware Region Extrication](https://mlsanity.com/paper/2508.13899/) (2025)
- [Scale-aware Adaptive Supervised Network with Limited Medical Annotations](https://mlsanity.com/paper/2601.01005/) (2026)
- [AKGNet: Attribute Knowledge-Guided Unsupervised Lung-Infected Area Segmentation](https://mlsanity.com/paper/2404.11008/) (2024)
- [Cross-model Mutual Learning for Exemplar-based Medical Image Segmentation](https://mlsanity.com/paper/2404.11812/) (2024)
- [Improving Prostate Gland Segmentation Using Transformer based Architectures](https://mlsanity.com/paper/2506.14844/) (2025)

## Abstract (arXiv)

Medical image segmentation is one of the most fundamental tasks concerning medical information analysis. Various solutions have been proposed so far, including many deep learning-based techniques, such as U-Net, FC-DenseNet, etc. However, high-precision medical image segmentation remains a highly challenging task due to the existence of inherent magnification and distortion in medical images as well as the presence of lesions with similar density to normal tissues. In this paper, we propose TFCNs (Transformers for Fully Convolutional denseNets) to tackle the problem by introducing ResLinear-Transformer (RL-Transformer) and Convolutional Linear Attention Block (CLAB) to FC-DenseNet. TFCNs is not only able to utilize more latent information from the CT images for feature extraction, but also can capture and disseminate semantic features and filter non-semantic features more effectively through the CLAB module. Our experimental results show that TFCNs can achieve state-of-the-art performance with dice scores of 83.72% on the Synapse dataset. In addition, we evaluate the robustness of TFCNs for lesion area effects on the COVID-19 public datasets. The Python code will be made publicly available on https://github.com/HUANGLIZI/TFCNs.