mlsanityData to 7 Oct 2026

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

arXiv 2203.10726 ↗First posted 21 Mar 2022eess.IV cs.CVcited by 21

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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.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 16 of its 34 references that are in the map. First posted on arXiv 21 Mar 2022.

Builds on

16 of 34 references.

References · 16

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  5. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
  6. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
  7. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
  8. 2017Deep Layer Aggregationcited by 436
  9. 2021UTNet: A Hybrid Transformer Architecture for Medical Image Segmentationcited by 116
  10. 2017Structured Attention Networkscited by 95
  11. 2020Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptationcited by 81
  12. 2021Multi-Compound Transformer for Accurate Biomedical Image Segmentationcited by 42
4 more
  1. 2018CR-GAN: Learning Complete Representations for Multi-view Generationcited by 23
  2. 2022Region Proposal Rectification Towards Robust Instance Segmentation of Biological Imagescited by 13
  3. 2021Right Ventricular Segmentation from Short- and Long-Axis MRIs via Information Transitioncited by 9
  4. 2019Effective 3D Humerus and Scapula Extraction using Low-contrast and High-shape-variability MR Datacited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 8; 2024: 8; 2025: 3; 2026: 1.

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  1. 2023Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentationcited by 32
  2. 2023A Recent Survey of Vision Transformers for Medical Image Segmentationcited by 22
  3. 2022DeepRecon: Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via A Structure-Specific Generative Methodcited by 17
  4. 2025Show and Segment: Universal Medical Image Segmentation via In-Context Learningcited by 15
  5. 2024Layout Agnostic Scene Text Image Synthesis with Diffusion Modelscited by 14
  6. 2023Dealing With Heterogeneous 3D MR Knee Images: A Federated Few-Shot Learning Method With Dual Knowledge Distillationcited by 11
  7. 2023DeFormer: Integrating Transformers with Deformable Models for 3D Shape Abstraction from a Single Imagecited by 11
  8. 2023DMCVR: Morphology-Guided Diffusion Model for 3D Cardiac Volume Reconstructioncited by 10
  9. 2025LUCAS: Layered Universal Codec Avatarscited by 7
  10. 2023Deep Deformable Models: Learning 3D Shape Abstractions with Part Consistencycited by 5
  11. 2023GL-Fusion: Global-Local Fusion Network for Multi-view Echocardiogram Video Segmentationcited by 4
  12. 2024CAMS: Convolution and Attention-Free Mamba-based Cardiac Image Segmentationcited by 4
9 more
  1. 2024EchoONE: Segmenting Multiple echocardiography Planes in One Modelcited by 4
  2. 2023TranSOP: Transformer-based Multimodal Classification for Stroke Treatment Outcome Predictioncited by 3
  3. 2024Instantaneous Perception of Moving Objects in 3Dcited by 3
  4. 2024Crop and Couple: cardiac image segmentation using interlinked specialist networkscited by 2
  5. 2024Resolving Inconsistent Semantics in Multi-Dataset Image Segmentationcited by 1
  6. 2024Compositional Segmentation of Cardiac Images Leveraging Metadatacited by 1
  7. 2025Large Sign Language Models: Toward 3D American Sign Language Translationcited by 1
  8. 2024Multi-view Cardiac Image Segmentation via Trans-Dimensional Priorscited by 0
  9. 2026DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentationcited by 0