ArticleDiagnostics (Basel, Switzerland)2021
TransMed: Transformers Advance Multi-Modal Medical Image Classification.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 113 papers, 1 of them a synthesis that pooled it.
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Who cites it
113 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applying transformer-based deep learning models in image-driven cancer diagnosis: a comprehensive bibliometric analysis of global research trends.Frontiers in oncology · 2026Pooled it
- Dual-level knowledge distillation from vision transformer to Swin transformer for fine-grained brain tumor classification: A 44-class MRI benchmark.Journal of pathology informatics · 2026Article
- Multimodal transformers predict cancer therapy response from tumor mechanics.Array (New York, N.Y.) · 2026Article
- An interpretable multi-instance learning method for accurate differentiation of malignant and benign laryngeal lesions in laryngoscopy.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- TransSensors (Basel, Switzerland) · 2026Article
- Benchmarking Hybrid CNN-Transformer Versus Pure Transformer Architectures for Accelerated HyperpolarizedJournal of magnetic resonance imaging : JMRI · 2026Article
- ETDACVO: Structural-Fidelity-Aware Evolutionary Co-Optimization for Robust and Explainable Brain Tumor MRI Classification.Biomedicines · 2026Article
- FLAME: Federated Learning and Aggregated Multi-Model Ensemble for Multi-Class Alzheimer's Disease Stage Classification from Structured Clinical Data.Diagnostics (Basel, Switzerland) · 2026Article
- Hybrid vision transformer and ensemble machine learning framework for automated atherosclerotic plaque classification in intravascular ultrasound imaging.Scientific reports · 2026Article
- Stratifying amyloid burden in early Alzheimer's disease using cascaded attention-guided vision transformer using [¹⁸F]Florbetapir PET.European radiology · 2026Article
- RNNet-MST: A ResNet-50 with Multi-Scale Transformer Blocks for Pulmonary Nodule Classification and Attention-Based Localization on Chest X-Ray Images.Diagnostics (Basel, Switzerland) · 2026Article
- Foundation models in healthcare: a comprehensive review from technical advances to clinical translation.Journal of translational medicine · 2026Review
- LungDxFormer: a transformer-CNN hybrid model with dynamic spatial attention for accurate lung cancer detection and classification.Scientific reports · 2026Article
- Comprehensive deep learning-assisted multi-condition analysis of knee MRI studies improves resident radiologist performance.European radiology · 2026Article
- CT-to-MRI translation of medical volume data based on an enhanced diffusion model.Scientific reports · 2026Article
- Multi-plane vision transformer for hemorrhage classification using axial and sagittal MRI data.Scientific reports · 2026Article
- Review
- SIG-CFFNet: Structural Information-Guided Cascaded Feature Fusion Network for Gastrointestinal Anatomy Classification.Annals of biomedical engineering · 2026Article
- Development and validation of a transformer-based deep learning model for predicting distant metastasis in non-small cell lung cancer usingClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 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
53 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
Abstract
Over the past decade, convolutional neural networks (CNN) have shown very competitive performance in medical image analysis tasks, such as disease classification, tumor segmentation, and lesion detection. CNN has great advantages in extracting local features of images. However, due to the locality of convolution operation, it cannot deal with long-range relationships well. Recently, transformers have been applied to computer vision and achieved remarkable success in large-scale datasets. Compared with natural images, multi-modal medical images have explicit and important long-range dependencies, and effective multi-modal fusion strategies can greatly improve the performance of deep models. This prompts us to study transformer-based structures and apply them to multi-modal medical images. Existing transformer-based network architectures require large-scale datasets to achieve better performance. However, medical imaging datasets are relatively small, which makes it difficult to apply pure transformers to medical image analysis. Therefore, we propose TransMed for multi-modal medical image classification. TransMed combines the advantages of CNN and transformer to efficiently extract low-level features of images and establish long-range dependencies between modalities. We evaluated our model on two datasets, parotid gland tumors classification and knee injury classification. Combining our contributions, we achieve an improvement of 10.1% and 1.9% in average accuracy, respectively, outperforming other state-of-the-art CNN-based models. The results of the proposed method are promising and have tremendous potential to be applied to a large number of medical image analysis tasks. To our best knowledge, this is the first work to apply transformers to multi-modal medical image classification.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.