ReviewBMC medical imaging2024
A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound.
Review in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
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Who cites it
25 citing papers in PubMed.
- Improving Video-Based Prediction of Cerebellar Ataxia Severity Using a Pretrained Deep Learning Model.Brain and behavior · 2026Article
- Multicohort Evaluation of Acquisition Confounding in Single-Site Lung-CT Malignancy Classification with Frozen Foundation-Model Features.Journal of imaging informatics in medicine · 2026Article
- SOTS: Leveraging Self-supervised Pretraining for Label-Efficient Ovarian Tumor Segmentation in Ultrasound Images.Journal of imaging informatics in medicine · 2026Article
- Medical code embeddings from claims-based co-occurrences: a unified semantic space for ICD-10 diagnoses and ATC medications.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Theranostic Smart Bionanosensors for Solid Tumors: Advances, Challenges, and Future Directions.Applied biochemistry and biotechnology · 2026Review
- Overview of State-of-the-Art Learning-Based Classification Methods in Medical Imaging.Annals of biomedical engineering · 2026Review
- Comparative Evaluation of Deep Learning and Foundation Model Embeddings for Osteoarthritis Feature Classification in Knee Radiographs.Journal of imaging informatics in medicine · 2026Article
- A modern radiologist's guide to artificial intelligence.Pediatric radiology · 2026Review
- Recurrent neural network long short term memory model to detect the pile toe using raw data of pile integrity test.Scientific reports · 2026Article
- A Systematic Review of Contrastive Learning in Medical AI: Foundations, Biomedical Modalities, and Future Directions.Bioengineering (Basel, Switzerland) · 2026Review
- Self-Supervised Transformer-Based Pipeline for Liver Tumor Segmentation and Type Classification.JCO clinical cancer informatics · 2026Article
- Band-limited implicit neural representations for diffusion-weighted imaging denoising.Physics in medicine and biology · 2026Article
- Vision Foundry: A System for Training Foundational Vision AI Models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Foundation Models Meet Medical Image Interpretation.Research (Washington, D.C.) · 2026Review
- Privacy-Aware Continual Self-Supervised Learning on Multi-Window Chest Computed Tomography for Domain-Shift Robustness.Bioengineering (Basel, Switzerland) · 2025Article
- Self-supervised learning with BYOL for anterior cruciate ligament tear detection from knee MRI.MethodsX · 2025Article
- Improving surgical phase recognition using self-supervised deep learning.Scientific reports · 2025Article
- Multi scale self supervised learning for deep knowledge transfer in diabetic retinopathy grading.Scientific reports · 2025Article
- The Efficacy of Semantics-Preserving Transformations in Self-Supervised Learning for Medical Ultrasound.Bioengineering (Basel, Switzerland) · 2025Article
- Self-supervised identification and elimination of harmful datasets in distributed machine learning for medical image analysis.NPJ digital medicine · 2025Article
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
Abstract
Self-supervised pretraining has been observed to be effective at improving feature representations for transfer learning, leveraging large amounts of unlabelled data. This review summarizes recent research into its usage in X-ray, computed tomography, magnetic resonance, and ultrasound imaging, concentrating on studies that compare self-supervised pretraining to fully supervised learning for diagnostic tasks such as classification and segmentation. The most pertinent finding is that self-supervised pretraining generally improves downstream task performance compared to full supervision, most prominently when unlabelled examples greatly outnumber labelled examples. Based on the aggregate evidence, recommendations are provided for practitioners considering using self-supervised learning. Motivated by limitations identified in current research, directions and practices for future study are suggested, such as integrating clinical knowledge with theoretically justified self-supervised learning methods, evaluating on public datasets, growing the modest body of evidence for ultrasound, and characterizing the impact of self-supervised pretraining on generalization.
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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.