ReviewBiomedical engineering online2024
Advantages of transformer and its application for medical image segmentation: a survey.
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41 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Landscape of 2D Deep Learning Segmentation Networks Applied to CT Scan from Lung Cancer Patients: A Systematic Review.Journal of imaging informatics in medicine · 2025Pooled it
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- SwiftMSeg: lightweight multi-scale local-global context modeling with transformer for medical image segmentation.Scientific reports · 2026Article
- OcuViT: A Vision Transformer-Based Approach for Automated Diabetic Retinopathy and AMD Classification.Journal of imaging informatics in medicine · 2026Article
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- MDVM-UNet: lumbar MRI segmentation and lordosis angle measurement via a dual-driven mechanism.BMC medical imaging · 2026Article
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- Integrating tumor habitat heterogeneity with a hybrid deep learning architecture for ultrasound radiomics: a dual-center study on non-invasive prediction of PD-L1 expression in triple-negative breast cancer.Breast cancer research : BCR · 2026Article
- AutoPrompt-SAM3D: integrated generation and selection for SAM2-based 3D medical segmentation.BMC bioinformatics · 2026Article
- Advanced hybrid transformer CNN framework for improved skin lesion classification and segmentation.Scientific reports · 2026Article
- Dual-encoder 3D transformer-based U-Net with temporal attention for non-mass enhancement segmentation in breast dynamic contrast-enhanced MRI.Radiological physics and technology · 2026Article
- AI assistance improves radiology resident reader performance in CT diagnosis of intracranial hemorrhage.La Radiologia medica · 2026Article
- Deep learning-based multimodal fusion of MRI and whole slide image for predicting neoadjuvant therapy response in locally advanced head and neck squamous cell carcinoma.BMC medical imaging · 2026Article
- A Comparative Evaluation of Microimpedance Tomography Reconstruction Algorithms for in Vitro Imaging.ACS sensors · 2025Article
- RepSAU-Net: Semantic Segmentation of Barcodes in Complex Backgrounds via Fused Self-Attention and Reparameterization Methods.Journal of imaging · 2025Article
- Development of a unified deep learning approach integrating CNN-based local and ViT-based global feature extraction for enhanced cotton disease and pest classification.Plant methods · 2025Article
- Clinical Application of Vision Transformers for Melanoma Classification: A Multi-Dataset Evaluation Study.Cancers · 2025Article
- Research on the digital transaction model of the sports industry chain based on blockchain technology.Scientific reports · 2025Article
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5 authors.
Funding
Abstract
purposeConvolution operator-based neural networks have shown great success in medical image segmentation over the past decade. The U-shaped network with a codec structure is one of the most widely used models. Transformer, a technology used in natural language processing, can capture long-distance dependencies and has been applied in Vision Transformer to achieve state-of-the-art performance on image classification tasks. Recently, researchers have extended transformer to medical image segmentation tasks, resulting in good models.
methodsThis review comprises publications selected through a Web of Science search. We focused on papers published since 2018 that applied the transformer architecture to medical image segmentation. We conducted a systematic analysis of these studies and summarized the results.
resultsTo better comprehend the benefits of convolutional neural networks and transformers, the construction of the codec and transformer modules is first explained. Second, the medical image segmentation model based on transformer is summarized. The typically used assessment markers for medical image segmentation tasks are then listed. Finally, a large number of medical segmentation datasets are described.
conclusionEven if there is a pure transformer model without any convolution operator, the sample size of medical picture segmentation still restricts the growth of the transformer, even though it can be relieved by a pretraining model. More often than not, researchers are still designing models using transformer and convolution operators.
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