ArticleFrontiers in oncology2022
Swin transformer-based GAN for multi-modal medical image translation.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 88 citations in OpenAlex.
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- CT-to-MRI translation of medical volume data based on an enhanced diffusion model.Scientific reports · 2026Article
- Hierarchical Multi-Scale Feature Fusion Network with Implicit Neural Representation and Mamba for Cross-Modality MRI Synthesis.Sensors (Basel, Switzerland) · 2026Article
- Score-based generative diffusion models to synthesize full-dose FDG brain PET from MRI in epilepsy patients.Frontiers in artificial intelligence · 2026Article
- ViT-Stain: Vision transformer-driven virtual staining for skin histopathology via global contextual learning.PloS one · 2026Article
- Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends.MedComm · 2025Review
- Based on TransRes-Pix2Pix network to generate the OBL image during SMILE surgery.Frontiers in cell and developmental biology · 2025Article
- Robust deep MRI contrast synthesis using a prior-based and task-oriented 3D network.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- A multi-modal deep learning solution for precise pneumonia diagnosis: the PneumoFusion-Net model.Frontiers in physiology · 2025Article
- Transformers and large language models in healthcare: A review.Artificial intelligence in medicine · 2024Review
- L2NLF: a novel linear-to-nonlinear framework for multi-modal medical image registration.Biomedical engineering letters · 2024Article
- Exceeding the limit for microscopic image translation with a deep learning-based unified framework.PNAS nexus · 2024Article
- A Deep Diagnostic Framework Using Explainable Artificial Intelligence and Clustering.Diagnostics (Basel, Switzerland) · 2023Article
- Article
- Decomposition of musculoskeletal structures from radiographs using an improved CycleGAN framework.Scientific reports · 2023Article
- SW-UNet: a U-Net fusing sliding window transformer block with CNN for segmentation of lung nodules.Frontiers in medicine · 2023Article
- The Role of Artificial Intelligence in Epiretinal Membrane Care: A Scoping Review.Ophthalmology scienceReview
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medical image-to-image translation is considered a new direction with many potential applications in the medical field. The medical image-to-image translation is dominated by two models, including supervised Pix2Pix and unsupervised cyclic-consistency generative adversarial network (GAN). However, existing methods still have two shortcomings: 1) the Pix2Pix requires paired and pixel-aligned images, which are difficult to acquire. Nevertheless, the optimum output of the cycle-consistency model may not be unique. 2) They are still deficient in capturing the global features and modeling long-distance interactions, which are critical for regions with complex anatomical structures. We propose a Swin Transformer-based GAN for Multi-Modal Medical Image Translation, named MMTrans. Specifically, MMTrans consists of a generator, a registration network, and a discriminator. The Swin Transformer-based generator enables to generate images with the same content as source modality images and similar style information of target modality images. The encoder part of the registration network, based on Swin Transformer, is utilized to predict deformable vector fields. The convolution-based discriminator determines whether the target modality images are similar to the generator or from the real images. Extensive experiments conducted using the public dataset and clinical datasets showed that our network outperformed other advanced medical image translation methods in both aligned and unpaired datasets and has great potential to be applied in clinical applications.
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