ArticleRadiology. Artificial intelligence2022
Visual Transformers and Convolutional Neural Networks for Disease Classification on Radiographs: A Comparison of Performance, Sample Efficiency, and Hidden Stratification.
Article in Radiology. Artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.
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20 citing papers in PubMed, 3 syntheses or guidelines pooled it, 40 citations in OpenAlex.
- Evaluating vision transformers and convolutional neural networks in the context of dental image processing: a systematic review.BMC oral health · 2025Pooled it
- Systematic Review of Hybrid Vision Transformer Architectures for Radiological Image Analysis.Journal of imaging informatics in medicine · 2025Pooled it
- Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review.Journal of medical systems · 2024Pooled it
- Deep learning-based renal-trauma classification: comparison of two-dimensional convolutional neural network and vision-transformer models on arterial- and venous-phase computed-tomography images.Radiological physics and technology · 2026Article
- Sharpness matters: Higher image resolution improves generalization and explainability in chest X-ray classification.Scientific reports · 2026Article
- An Adaptive Deep Learning Framework for Multi-Label Chest X-Ray Diagnosis Using a Hybrid CNN-Transformer Architecture and Class-Wise Ensemble Fusion.Diagnostics (Basel, Switzerland) · 2026Article
- Enhanced gastrointestinal disease classification using a convvit hybrid model on endoscopic images.Physical and engineering sciences in medicine · 2025Article
- Addressing data heterogeneity in distributed medical imaging with heterosync learning.Nature communications · 2025Article
- Images Versus Videos in Contrast-Enhanced Ultrasound for Computer-Aided Diagnosis.Sensors (Basel, Switzerland) · 2025Article
- Medical slice transformer for improved diagnosis and explainability on 3D medical images with DINOv2.Scientific reports · 2025Article
- Review
- Preparing for downstream tasks in artificial intelligence for dental radiology: a baseline performance comparison of deep learning models.Dento maxillo facial radiology · 2025Article
- Multimodal integration strategies for clinical application in oncology.Frontiers in pharmacology · 2025Review
- Deep Learning of radiology-genomics integration for computational oncology: A mini review.Computational and structural biotechnology journal · 2024Review
- Prediction of visceral pleural invasion of clinical stage I lung adenocarcinoma using thoracoscopic images and deep learning.Surgery today · 2024Article
- Evaluating the Robustness of a Deep Learning Bone Age Algorithm to Clinical Image Variation Using Computational Stress Testing.Radiology. Artificial intelligence · 2024Article
- Vision transformer to differentiate between benign and malignant slices inScientific reports · 2024Article
- Performance of ChatGPT on the Brazilian Radiology and Diagnostic Imaging and Mammography Board Examinations.Radiology. Artificial intelligence · 2024Article
- Towards reproducible radiomics research: introduction of a database for radiomics studies.European radiology · 2024Article
- New trend in artificial intelligence-based assistive technology for thoracic imaging.La Radiologia medica · 2023Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To compare performance, sample efficiency, and hidden stratification of visual transformer (ViT) and convolutional neural network (CNN) architectures for diagnosis of disease on chest radiographs and extremity radiographs using transfer learning. Materials and Methods: In this HIPAA-compliant retrospective study, the authors fine-tuned data-efficient image transformers (DeiT) ViT and CNN classification models pretrained on ImageNet using the National Institutes of Health Chest X-ray 14 dataset (112 120 images) and MURA dataset (14 656 images) for thoracic disease and extremity abnormalities, respectively. Performance was assessed on internal test sets and 75 000 external chest radiographs (three datasets). The primary comparison was DeiT-B ViT vs DenseNet121 CNN; secondary comparisons included DeiT-Ti (Tiny), ResNet152, and EfficientNetB7. Sample efficiency was evaluated by training models on varying dataset sizes. Hidden stratification was evaluated by comparing prevalence of chest tubes in pneumothorax false-positive and false-negative predictions and specific abnormalities for MURA false-negative predictions. Results: DeiT-B weighted area under the receiver operating characteristic curve (wAUC) was slightly lower than that for DenseNet121 on chest radiograph (0.78 vs 0.79; Conclusion: Although DeiT models had lower wAUCs than CNNs for chest radiograph and extremity domains, the differences may be negligible in clinical practice. DeiT-B had sample efficiency similar to that of DenseNet121 and may be less susceptible to certain types of hidden stratification.
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