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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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Related Experiment Video

Updated: Nov 2, 2025

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Weakly Supervised Deep Learning Approach to Breast MRI Assessment.

Michael Z Liu¹, Cara Swintelski², Shawn Sun³

¹Department of Medical Physics, Columbia University Medical Center, New York, NY 10032-3784.

Academic Radiology

|June 10, 2021

Summary

This study demonstrates a weakly supervised deep learning method for breast MRI analysis. The approach achieves high specificity in classifying malignant versus benign breast lesions without pixel-level segmentation.

Keywords:

Deep learning breast MRI breast cancer neural network weakly supervised

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Area of Science:

Radiology
Artificial Intelligence
Medical Imaging

Background:

Breast cancer diagnosis relies heavily on Magnetic Resonance Imaging (MRI).
Accurate classification of breast MRI lesions is crucial for patient management.
Current methods may require detailed segmentation, increasing complexity.

Purpose of the Study:

To evaluate a weakly supervised deep learning (DL) approach for breast MRI assessment.
To determine if DL can improve lesion classification specificity without pixel-level segmentation.
To assess the feasibility of this automated approach in clinical settings.

Main Methods:

A Resnet-101 based weakly supervised network was developed.
The model was trained on 278,685 breast MRI image slices from 438 patients.
Performance was evaluated using a held-out test set of 13,024 images.

Main Results:

The DL model achieved an Area Under the Curve (AUC) of 0.92.
Overall accuracy was 94.2%, with a specificity of 95.3% for distinguishing malignant from benign lesions.
Sensitivity was 74.4%.

Conclusions:

Weakly supervised DL is a feasible method for breast MRI assessment.
This approach enhances specificity in lesion classification without requiring pixel-level segmentation.
The findings suggest potential for improved diagnostic accuracy in breast MRI analysis.