ArticleScientific reports2023
Self-supervised pre-training with contrastive and masked autoencoder methods for dealing with small datasets in deep learning for medical imaging.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 43 citations in OpenAlex.
- Limited-view signal recovery with frequency-aware denoising diffusion and geometry-integrated masked autoencoders for X-ray acoustic computed tomography.Photoacoustics · 2026Article
- Surgical Video Understanding with Alignment-Preserving Temporal Adaptation and Action Triplet Text Alignment.Bioengineering (Basel, Switzerland) · 2026Article
- Foundation models for radiology: fundamentals, applications, opportunities, challenges, risks, and prospects.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Review
- Human versus artificial intelligence in oral pathology diagnosis: a comparative study of ChatGPT, Grok, and MANUS.Scientific reports · 2026Article
- Artificial intelligence for colposcopic and cytological image analysis in early cervical cancer detection.iScience · 2026Review
- US-ATHC: Unsupervised Multi-Class Glioma Segmentation via Adaptive Thresholding and Clustering.Biomedicines · 2026Article
- Pretraining on Chronic Lung Inflammatory Disease Datasets to Enhance Indeterminant Lung Cancer Classification using Masked Autoencoders.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- Anatomically-guided masked autoencoder pre-training for aneurysm detection.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision · 2026Article
- Privacy-Aware Continual Self-Supervised Learning on Multi-Window Chest Computed Tomography for Domain-Shift Robustness.Bioengineering (Basel, Switzerland) · 2025Article
- Study on Ultrasound-Assisted Diagnosis of CHB Complicated with NAFLD Hepatic Fibrosis Based on Deep Learning.Journal of imaging informatics in medicine · 2025Article
- Comparative analysis of supervised and self-supervised learning with small and imbalanced medical imaging datasets.Scientific reports · 2025Article
- Foundation models for radiology-the position of the AI for Health Imaging (AI4HI) network.Insights into imaging · 2025Review
- The Evolution of Radiology Image Annotation in the Era of Large Language Models.Radiology. Artificial intelligence · 2025Review
- Article
- Machine learning of automatic hierarchical multi-label classification method for identifying metal failure mechanisms.Scientific reports · 2025Article
- A unified approach to medical image segmentation by leveraging mixed supervision and self and transfer learning (MIST).Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2025Article
- Detection of Ocular Surface Squamous Neoplasia Using Artificial Intelligence With Anterior Segment Optical Coherence Tomography.American journal of ophthalmology · 2025Article
- Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation.Diagnostics (Basel, Switzerland) · 2025Review
- Masked autoencoder of multi-scale convolution strategy combined with knowledge distillation for facial beauty prediction.Scientific reports · 2025Article
- Contrastive self-supervised learning for neurodegenerative disorder classification.Frontiers in neuroinformatics · 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
7 authors at 2 institutions in 1 country.
Funding
Abstract
Deep learning in medical imaging has the potential to minimize the risk of diagnostic errors, reduce radiologist workload, and accelerate diagnosis. Training such deep learning models requires large and accurate datasets, with annotations for all training samples. However, in the medical imaging domain, annotated datasets for specific tasks are often small due to the high complexity of annotations, limited access, or the rarity of diseases. To address this challenge, deep learning models can be pre-trained on large image datasets without annotations using methods from the field of self-supervised learning. After pre-training, small annotated datasets are sufficient to fine-tune the models for a specific task. The most popular self-supervised pre-training approaches in medical imaging are based on contrastive learning. However, recent studies in natural image processing indicate a strong potential for masked autoencoder approaches. Our work compares state-of-the-art contrastive learning methods with the recently introduced masked autoencoder approach "SparK" for convolutional neural networks (CNNs) on medical images. Therefore, we pre-train on a large unannotated CT image dataset and fine-tune on several CT classification tasks. Due to the challenge of obtaining sufficient annotated training data in medical imaging, it is of particular interest to evaluate how the self-supervised pre-training methods perform when fine-tuning on small datasets. By experimenting with gradually reducing the training dataset size for fine-tuning, we find that the reduction has different effects depending on the type of pre-training chosen. The SparK pre-training method is more robust to the training dataset size than the contrastive methods. Based on our results, we propose the SparK pre-training for medical imaging tasks with only small annotated datasets.
Indexed as
Identifiers
What OpenQuestion holds
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.