SynthesisJournal of medical systems2024
Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review.
Synthesis in Journal of medical systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
110 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic performance of PET-based artificial intelligence for differentiating Parkinson's disease from normal controls or atypical parkinsonism: a systematic review and meta-analysis.Journal of neurology · 2025Pooled it
- Evaluating vision transformers and convolutional neural networks in the context of dental image processing: a systematic review.BMC oral health · 2025Pooled it
- Combined Model Integrating Clinical-Imaging and Deep Learning to Discriminate Between Persistent Inflammatory and Malignant Subsolid Pulmonary Nodules.Journal of imaging informatics in medicine · 2026Article
- TransUNet-GradCAM: a hybrid transformer-U-Net with self-attention and explainable visualizations for foot ulcer segmentation.BMC medical imaging · 2026Article
- Classification of Foot-and-Mouth and Lumpy Skin Disease in Cattle Using Frozen Pretrained Encoders: A Comparison of Convolutional and Transformer Architectures.Veterinary sciences · 2026Article
- AI-Based Spatial Burn Assessment with MLLMs: Body Region, 3 × 3 Grid Classification and Burn Instance Counting from Photographs and Semantic Segmentation Masks.Biomedicines · 2026Article
- Foundation model-powered deep learning of endometrial histology for predicting the cumulative live birth of an in vitro fertilization cycle.PLOS digital health · 2026Article
- Deep learning aging marker from retinal images unveils sex-specific clinical and genetic signatures.Nature communications · 2026Article
- Comparative Study of CNN, Hybrid, and Transformer Architectures in Medical Image Classification.Journal of imaging informatics in medicine · 2026Article
- PancDS in Real-World Practice: A Prospective Multicenter Validation of a Clinical Decision-Support System Bridging Experience Gaps in Pancreatic Lesion Diagnosis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Cascaded deep learning for automated lung cancer tumour burden quantification on [Quantitative imaging in medicine and surgery · 2026Article
- Deep learning-based detection of acute pancreatitis on abdominal contrast-enhanced CT.European radiology experimental · 2026Article
- Automated forensic human identification on dental panoramic radiographs using transformer-based detection with supervised metric learning.BMC oral health · 2026Article
- ViMST: vision transformer-based dual modality multi-task graph contrastive network for spatial transcriptomics microenvironments investigation.BMC biology · 2026Article
- Multimodal spatial omics: From data acquisition to computational integration.Patterns (New York, N.Y.) · 2026Review
- Attention-Driven CNNs as a Strong Default for HER2 Prediction from DCE-MRI: A Comparison with Transformer Architectures.Bioengineering (Basel, Switzerland) · 2026Article
- [Colorectal cancer diagnosis method based on dynamic gland-aware and tissue soft-clustering].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Machine learning-driven prediction of healthcare resource trends and optimal allocation strategies: a data-driven approach.BMC health services research · 2026Article
- A functionally guided fusion Vision Transformer for predicting IDH status in gliomas: a multicenter study with external validation and incomplete multimodal evaluation.Radiologie (Heidelberg, Germany) · 2026Article
- Overview of State-of-the-Art Learning-Based Classification Methods in Medical Imaging.Annals of biomedical engineering · 2026Review
50 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
Abstract
In the rapidly evolving field of medical image analysis utilizing artificial intelligence (AI), the selection of appropriate computational models is critical for accurate diagnosis and patient care. This literature review provides a comprehensive comparison of vision transformers (ViTs) and convolutional neural networks (CNNs), the two leading techniques in the field of deep learning in medical imaging. We conducted a survey systematically. Particular attention was given to the robustness, computational efficiency, scalability, and accuracy of these models in handling complex medical datasets. The review incorporates findings from 36 studies and indicates a collective trend that transformer-based models, particularly ViTs, exhibit significant potential in diverse medical imaging tasks, showcasing superior performance when contrasted with conventional CNN models. Additionally, it is evident that pre-training is important for transformer applications. We expect this work to help researchers and practitioners select the most appropriate model for specific medical image analysis tasks, accounting for the current state of the art and future trends in the field.
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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.