| Autor: |
Hyung S. Kim, Michael F. McNitt-Gray, William Hsu, Youngwon Choi, M Wahi-Anwar, Nastaran Emaminejad, Matthew S. Brown |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Medical Imaging 2021: Physics of Medical Imaging. |
| DOI: |
10.1117/12.2582126 |
| Popis: |
A novel physics-based data augmentation (PBDA) is introduced, to provide a representative approach to introducing variance during training of a deep-learning model. Compared to traditional geometric-based data augmentation (GBDA), we hypothesize that PBDA will provide more realistic variation representative of potential imaging conditions that may be seen beyond the initial training data, and thereby train a more robust model (particularly in the scope of medical imaging). PBDA is tested in the context of false-positive reduction in nodule detection in low-dose lung CT and is shown to exhibit superior performance and robustness across a wide range of imaging conditions. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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