# TransFusion: Multi-view Divergent Fusion for Medical Image Segmentation with Transformers

arXiv: https://arxiv.org/abs/2203.10726 · HTML: https://mlsanity.com/paper/2203.10726/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 21 Mar 2022.

## Builds on (most cited first, 16 of 16)

- [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)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [Deep Layer Aggregation](https://mlsanity.com/paper/1707.06484/) (2017)
- [UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation](https://mlsanity.com/paper/2107.00781/) (2021)
- [Structured Attention Networks](https://mlsanity.com/paper/1702.00887/) (2017)
- [Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation](https://mlsanity.com/paper/2006.16806/) (2020)
- [Multi-Compound Transformer for Accurate Biomedical Image Segmentation](https://mlsanity.com/paper/2106.14385/) (2021)
- [CR-GAN: Learning Complete Representations for Multi-view Generation](https://mlsanity.com/paper/1806.11191/) (2018)
- [Region Proposal Rectification Towards Robust Instance Segmentation of Biological Images](https://mlsanity.com/paper/2203.02846/) (2022)
- [Right Ventricular Segmentation from Short- and Long-Axis MRIs via Information Transition](https://mlsanity.com/paper/2109.02171/) (2021)
- [Effective 3D Humerus and Scapula Extraction using Low-contrast and High-shape-variability MR Data](https://mlsanity.com/paper/1902.08527/) (2019)

## Built on by (most cited first, 21 of 21)

- [Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation](https://mlsanity.com/paper/2306.02416/) (2023)
- [A Recent Survey of Vision Transformers for Medical Image Segmentation](https://mlsanity.com/paper/2312.00634/) (2023)
- [DeepRecon: Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via A Structure-Specific Generative Method](https://mlsanity.com/paper/2206.07163/) (2022)
- [Show and Segment: Universal Medical Image Segmentation via In-Context Learning](https://mlsanity.com/paper/2503.19359/) (2025)
- [Layout Agnostic Scene Text Image Synthesis with Diffusion Models](https://mlsanity.com/paper/2406.01062/) (2024)
- [DeFormer: Integrating Transformers with Deformable Models for 3D Shape Abstraction from a Single Image](https://mlsanity.com/paper/2309.12594/) (2023)
- [Dealing With Heterogeneous 3D MR Knee Images: A Federated Few-Shot Learning Method With Dual Knowledge Distillation](https://mlsanity.com/paper/2303.14357/) (2023)
- [DMCVR: Morphology-Guided Diffusion Model for 3D Cardiac Volume Reconstruction](https://mlsanity.com/paper/2308.09223/) (2023)
- [LUCAS: Layered Universal Codec Avatars](https://mlsanity.com/paper/2502.19739/) (2025)
- [Deep Deformable Models: Learning 3D Shape Abstractions with Part Consistency](https://mlsanity.com/paper/2309.01035/) (2023)
- [GL-Fusion: Global-Local Fusion Network for Multi-view Echocardiogram Video Segmentation](https://mlsanity.com/paper/2309.11144/) (2023)
- [CAMS: Convolution and Attention-Free Mamba-based Cardiac Image Segmentation](https://mlsanity.com/paper/2406.05786/) (2024)
- [EchoONE: Segmenting Multiple echocardiography Planes in One Model](https://mlsanity.com/paper/2412.02993/) (2024)
- [TranSOP: Transformer-based Multimodal Classification for Stroke Treatment Outcome Prediction](https://mlsanity.com/paper/2301.10829/) (2023)
- [Instantaneous Perception of Moving Objects in 3D](https://mlsanity.com/paper/2405.02781/) (2024)
- [Crop and Couple: cardiac image segmentation using interlinked specialist networks](https://mlsanity.com/paper/2402.09156/) (2024)
- [Resolving Inconsistent Semantics in Multi-Dataset Image Segmentation](https://mlsanity.com/paper/2409.09893/) (2024)
- [Compositional Segmentation of Cardiac Images Leveraging Metadata](https://mlsanity.com/paper/2410.23130/) (2024)
- [Large Sign Language Models: Toward 3D American Sign Language Translation](https://mlsanity.com/paper/2511.08535/) (2025)
- [Multi-view Cardiac Image Segmentation via Trans-Dimensional Priors](https://mlsanity.com/paper/2404.16708/) (2024)
- [DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation](https://mlsanity.com/paper/2607.29568/) (2026)

## Abstract (arXiv)

Combining information from multi-view images is crucial to improve the performance and robustness of automated methods for disease diagnosis. However, due to the non-alignment characteristics of multi-view images, building correlation and data fusion across views largely remain an open problem. In this study, we present TransFusion, a Transformer-based architecture to merge divergent multi-view imaging information using convolutional layers and powerful attention mechanisms. In particular, the Divergent Fusion Attention (DiFA) module is proposed for rich cross-view context modeling and semantic dependency mining, addressing the critical issue of capturing long-range correlations between unaligned data from different image views. We further propose the Multi-Scale Attention (MSA) to collect global correspondence of multi-scale feature representations. We evaluate TransFusion on the Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI (M&Ms-2) challenge cohort. TransFusion demonstrates leading performance against the state-of-the-art methods and opens up new perspectives for multi-view imaging integration towards robust medical image segmentation.