Comparison of Breast MRI Tumor Classification Using Human-Engineered Radiomics, Transfer Learning From Deep Convolutional Neural Networks, and Fusion Method

Digital image-based signatures of breast tumors may ultimately contribute to the design of patient-specific breast cancer diagnostics and treatments. Beyond traditional human-engineered computer vision methods, tumor classification methods using transfer learning from deep convolutional neural netwo...

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Bibliographic Details
Published in:Proceedings of the IEEE 2020-01, Vol.108 (1), p.163
Main Authors: Whitney, Heather M, Li, Hui, Yu, Ji, Liu, Peifang, Giger, Maryellen L
Format: Article
Language:English
Subjects:
Artificial neural networks
Breast
CAI
Classification
Classifiers
Computer assisted instruction
Computer vision
Diagnostic systems
Digital imaging
Digital signatures
Magnetic resonance imaging
Mammography
Medical imaging
Neural networks
NMR
Nuclear magnetic resonance
Radiomics
Tumors
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Summary:Digital image-based signatures of breast tumors may ultimately contribute to the design of patient-specific breast cancer diagnostics and treatments. Beyond traditional human-engineered computer vision methods, tumor classification methods using transfer learning from deep convolutional neural networks (CNNs) are actively under development. This article will first discuss our progress in using CNN-based transfer learning to characterize breast tumors for various diagnostic, prognostic, or predictive image-based tasks across multiple imaging modalities, including mammography, digital breast tomosynthesis, ultrasound (US), and magnetic resonance imaging (MRI), compared to both human-engineered feature-based radiomics and fusion classifiers created through combination of such features. Second, a new study is presented that reports on a comprehensive comparison of the classification performances of features derived from human-engineered radiomic features, CNN transfer learning, and fusion classifiers for breast lesions imaged with MRI. These studies demonstrate the utility of transfer learning for computer-aided diagnosis and highlight the synergistic improvement in classification performance using fusion classifiers.
ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2019.2950187