ArticleJournal of digital imaging2022
Analyzing Transfer Learning of Vision Transformers for Interpreting Chest Radiography.
Article in Journal of digital imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis 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.
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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.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review.Journal of medical systems · 2024Pooled it
- Investigating the Utility of Explainable Artificial Intelligence for Neuroimaging-Based Dementia Diagnosis and Prognosis.Human brain mapping · 2026Article
- DG-TTA: Out-of-Domain Medical Image Segmentation Through Augmentation, Descriptor-Driven Domain Generalization, and Test-Time Adaptation.Sensors (Basel, Switzerland) · 2025Article
- Retaking assessment system based on the inspiratory state of chest X-ray image.Radiological physics and technology · 2025Article
- Automated classification of chest X-rays: a deep learning approach with attention mechanisms.BMC medical imaging · 2025Article
- Enhanced Pneumonia Detection in Chest X-Rays Using Hybrid Convolutional and Vision Transformer Networks.Current medical imaging · 2025Article
- From Binary to Multi-Class Classification: A Two-Step Hybrid CNN-ViT Model for Chest Disease Classification Based on X-Ray Images.Diagnostics (Basel, Switzerland) · 2024Article
- Automated Screening of Hip X-rays for Osteoporosis by Singh's Index Using Machine Learning Algorithms.Indian journal of orthopaedics · 2024Article
- Deep multimodal saliency parcellation of cerebellar pathways: Linking microstructure and individual function through explainable multitask learning.Human brain mapping · 2024Article
- Prediction of visceral pleural invasion of clinical stage I lung adenocarcinoma using thoracoscopic images and deep learning.Surgery today · 2024Article
- Efficient pneumonia detection using Vision Transformers on chest X-rays.Scientific reports · 2024Article
- Advances in the field of developing biomarkers for re-irradiation: a how-to guide to small, powerful data sets and artificial intelligence.Expert review of precision medicine and drug development · 2024Article
- The effect of hair removal and filtering on melanoma detection: a comparative deep learning study with AlexNet CNN.PeerJ. Computer science · 2024Article
- Comparison between vision transformers and convolutional neural networks to predict non-small lung cancer recurrence.Scientific reports · 2023Article
- 3D-Vision-Transformer Stacking Ensemble for Assessing Prostate Cancer Aggressiveness from T2w Images.Bioengineering (Basel, Switzerland) · 2023Article
- Prediction of body weight from chest radiographs using deep learning with a convolutional neural network.Radiological physics and technology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Limited availability of medical imaging datasets is a vital limitation when using "data hungry" deep learning to gain performance improvements. Dealing with the issue, transfer learning has become a de facto standard, where a pre-trained convolution neural network (CNN), typically on natural images (e.g., ImageNet), is finetuned on medical images. Meanwhile, pre-trained transformers, which are self-attention-based models, have become de facto standard in natural language processing (NLP) and state of the art in image classification due to their powerful transfer learning abilities. Inspired by the success of transformers in NLP and image classification, large-scale transformers (such as vision transformer) are trained on natural images. Based on these recent developments, this research aims to explore the efficacy of pre-trained natural image transformers for medical images. Specifically, we analyze pre-trained vision transformer on CheXpert and pediatric pneumonia dataset. We use CNN standard models including VGGNet and ResNet as baseline models. By examining the acquired representations and results, we discover that transfer learning from the pre-trained vision transformer shows improved results as compared to pre-trained CNN which demonstrates a greater transfer ability of the transformers in medical imaging.
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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 Socratic graph.