Name
Title | ||
|---|---|---|
Comparison of Breast MRI Tumor Classification Using Human-Engineered Radiomics, Transfer Learning From Deep Convolutional Neural Networks, and Fusion Method |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/JPROC.2019.2950187 | Proceedings of the IEEE |
Keywords | DocType | Volume |
Feature extraction,Lesions,Biomedical imaging,Breast,Task analysis,Cancer,Magnetic resonance imaging | Journal | 108 |
Issue | ISSN | Citations |
1 | 0018-9219 | 2 |
PageRank | References | Authors |
0.44 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heather M. Whitney | 1 | 2 | 0.44 |
| Hui Li | 2 | 45 | 15.48 |
| Yu Ji | 3 | 2 | 1.12 |
| Peifang Liu | 4 | 4 | 0.80 |
| M.L. Giger | 5 | 3 | 0.85 |