ArticleBioengineering (Basel, Switzerland)2023
Improving Data-Efficiency and Robustness of Medical Imaging Segmentation Using Inpainting-Based Self-Supervised Learning.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Foundation models in radiology: a primer for pediatric radiologists.Pediatric radiology · 2026Review
- Clinical validation of fully automated (peri-)articular tissue analysis for assessing osteoarthritis progression: A narrative review.Osteoarthritis and cartilage open · 2026Review
- Using deep feature distances for evaluating the perceptual quality of MR image reconstructions.Magnetic resonance in medicine · 2025Article
- A Systematic Review and Implementation Guidelines of Multimodal Foundation Models in Medical Imaging.Research square · 2025Article
- A vision-language foundation model for the generation of realistic chest X-ray images.Nature biomedical engineering · 2025Article
- Foundation Models in Radiology: What, How, Why, and Why Not.Radiology · 2025Review
- Self-Supervised Learning Improves Accuracy and Data Efficiency for IMU-Based Ground Reaction Force Estimation.IEEE transactions on bio-medical engineering · 2024Article
- Self-Supervised Learning Improves Accuracy and Data Efficiency for IMU-Based Ground Reaction Force Estimation.bioRxiv : the preprint server for biology · 2024Article
- AI in MRI: Computational Frameworks for a Faster, Optimized, and Automated Imaging Workflow.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
We systematically evaluate the training methodology and efficacy of two inpainting-based pretext tasks of context prediction and context restoration for medical image segmentation using self-supervised learning (SSL). Multiple versions of self-supervised U-Net models were trained to segment MRI and CT datasets, each using a different combination of design choices and pretext tasks to determine the effect of these design choices on segmentation performance. The optimal design choices were used to train SSL models that were then compared with baseline supervised models for computing clinically-relevant metrics in label-limited scenarios. We observed that SSL pretraining with context restoration using 32 × 32 patches and Poission-disc sampling, transferring only the pretrained encoder weights, and fine-tuning immediately with an initial learning rate of 1 × 10-3 provided the most benefit over supervised learning for MRI and CT tissue segmentation accuracy (
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