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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Updated: Jun 30, 2026

Real-time Video Projection in an MRI for Characterization of Neural Correlates Associated with Mirror Therapy for Phantom Limb Pain

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MedNet-FS: a few-shot learning framework for 3D MRI-based knee injury classification.

Xu Lu¹, Hongming Lin¹, Shanhua Sun²

¹Department of Orthopedics, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, Hubei, 441000, China.

Scientific Reports

|June 1, 2026

Summary

This summary is machine-generated.

Deep learning for knee MRI analysis is limited by data scarcity. This study introduces MedNet-FS, a few-shot learning framework achieving competitive ACL tear detection with minimal data, outperforming generic methods.

Keywords:

3D MRI ACL tear detection Data-efficient learning Diagnostic imaging Domain-specific pretraining Few-shot learning GE2E loss Knee injury classification MedNet-FS Medical image analysis

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Deep learning for 3D knee MRI analysis requires large annotated datasets, which are scarce.
Few-shot learning (FSL) shows potential for data-limited scenarios but is underexplored in volumetric medical imaging.

Purpose of the Study:

To introduce MedNet-FS, a 3D FSL framework for knee MRI analysis in data-scarce environments.
To evaluate the effectiveness of domain-specific pre-training and a Generalized End-to-End (GE2E) loss for enhancing FSL performance.

Main Methods:

Developed MedNet-FS, a 3D FSL framework integrating domain-specific pre-training on knee MRI data.
Utilized a Generalized End-to-End (GE2E) loss function.
Evaluated performance on internal (MRNet) and external (KneeMRI) datasets for ACL tear detection.

Main Results:

MedNet-FS significantly outperformed models with generic pre-training or standard cross-entropy loss.
Achieved an AUC of 0.76 for ACL tear detection on MRNet using only 40 samples per class.
Demonstrated generalizability on KneeMRI (AUC 0.62 for clear cases) but showed reduced performance on ambiguous partial tears (AUC 0.58).

Conclusions:

The combination of domain-specific pre-training and GE2E loss is crucial for effective FSL in knee MRI.
MedNet-FS provides a practical, scalable framework reducing annotation dependency for medical image analysis.
While not yet suitable for autonomous clinical use, it serves as a strong baseline for data-efficient deep learning in radiology.