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Deep-Learning-Based Semantic Labeling for 2D Mammography and Comparison of Complexity for Machine Learning Tasks
Paul H Yi1,2, Abigail Lin3, Jinchi Wei4
1The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 N. Caroline St., Room 4223, Baltimore, MD, 21287, USA. Pyi10@jhmi.edu.
Journal of Digital Imaging
|June 15, 2019
Summary
Deep convolutional neural networks (DCNNs) show promise for automated mammography analysis, accurately classifying views and laterality. However, breast density classification remains challenging, suggesting a need for larger datasets.
Area of Science:
- Radiology
- Medical Imaging
- Artificial Intelligence
Background:
- Machine learning (ML) offers potential for semantic labeling in medical imaging, aiming to enhance radiologist workflow and prioritize urgent cases.
- Deep convolutional neural networks (DCNNs) are a type of ML model increasingly applied to image analysis tasks.
Purpose of the Study:
- To develop DCNNs for automated classification of 2D mammography views, breast laterality, and breast tissue density.
- To compare the performance of DCNNs across these tasks of varying complexity.
Main Methods:
- Utilized 3034 2D mammographic images from the Digital Database for Screening Mammography.
- Trained DCNNs for classifying mammographic view, image laterality, and breast tissue density.
- Employed receiver-operating-characteristic (ROC) area under the curve (AUC) and accuracy to evaluate performance.
Main Results:
- The DCNN achieved an AUC of 1.0 for mammographic view classification.
- Breast laterality classification AUC improved from 0.75 to 0.93 after adjusting data augmentation.
- Breast density classification accuracy was 68%.
Conclusions:
- Automated semantic labeling of 2D mammography using DCNNs is feasible, even with limited data.
- Classifying breast density automatically presents greater challenges and may necessitate larger datasets.
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