KneePreM improves knee MRI analysis with less labeled data
KneePreM: Towards 3D Knee MRI Foundation Models via Large-Scale Unlabeled Pretraining and Label-Efficient Fine-Tuning
Computer Vision and Pattern Recognition
Summary
Analyzing knee MRI scans can be difficult because labeled examples are scarce even though many unlabeled scans exist. The authors created KneePreM, a computer model trained on many unlabeled knee MRIs that can then learn new tasks with fewer labeled images. This approach improved the accuracy of knee condition classification and tissue segmentation, especially when less labeled data was available. Their method outperformed previous models on several knee MRI tasks.
What this means in practice
- •For medical imaging software developers: Incorporate KneePreM to improve knee MRI diagnostic tools with less labeled data for better disease and tissue detection accuracy.$Commercial implications: Enables more accurate and label-efficient knee MRI analysis products for hospitals and radiology centers.
- •For radiology it teams: Deploy KneePreM to enhance existing workflows for knee MRI interpretation with faster training times and improved results when labeled data is limited.
Authors
Xinxin Wang, Liam Hazan, Jing Li, Simona Rabinovici-Cohen, Xiaojuan Li, Mingrui Yang
Abstract
Background: Large volumes of unlabeled knee MRI scans are available across repositories but remain insufficiently leveraged. We developed KneePreM, a knee-specific 3D self-supervised model, and evaluated transfer and label efficiency for classification and segmentation. Methods: A 3D U-Net masked autoencoder was pretrained on 19,011 unlabeled Osteoarthritis Initiative (OAI) MRI series from 4,791 participants. Downstream fine-tuning used full and reduced training sets for fastMRI+ two-label classification (1,172 examinations), Arthroscopic Partial Meniscectomy (APM) eight-target classification (1,716 examinations), SKM-TEA segmentation (155 examinations), and APM segmentation (25 examinations). Baselines were random initialization and SuPreM. Deployment workflow was implemented with a Model Context Protocol interface. Evaluation metrics included balanced accuracy, F1 score, ROC AUC, PR AUC, and Dice score. Statistical analysis used bootstrap confidence intervals and paired bootstrap tests for classification and Wilcoxon signed-rank tests for segmentation. Results: KneePreM achieved higher full-data macro ROC AUC than both baselines for fastMRI+ and APM (all p < .001). For fastMRI+ classification, KneePreM achieved a ROC AUC of 0.722 using 50% of the training data, exceeding both full-data baselines. In APM classification, KneePreM reached a ROC AUC of 0.740 with 70% of the data, matching the full-data random baseline and outperforming SuPreM. For SKM-TEA segmentation, its 70%-data Dice of 0.838 exceeded the full-data random baseline (0.835) and both same-budget comparators. In APM segmentation, its 75%-data Dice of 0.746 exceeded the full-data random baseline (0.731) and both same-budget comparators. Conclusion: KneePreM improves transfer performance and label efficiency across knee MRI classification and segmentation tasks, particularly when labeled training data are limited.