Domain generalization in deep learning based mass detection in mammography: A large-scale multi-center study
| Autor: | Lidia Garrucho, Kaisar Kushibar, Socayna Jouide, Oliver Diaz, Laura Igual, Karim Lekadir |
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| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Medicine (miscellaneous) Breast Neoplasms Processament digital d'imatges Càncer de mama Machine Learning (cs.LG) Machine Learning Deep Learning Breast cancer Artificial Intelligence Aprenentatge automàtic Machine learning FOS: Electrical engineering electronic engineering information engineering Humans Image and Video Processing (eess.IV) Electrical Engineering and Systems Science - Image and Video Processing Mamografia 68T07 68U10 65D17 Diagnòstic per la imatge Diagnostic imaging Female Neural Networks Computer Digital image processing Mammography |
| Zdroj: | Artificial Intelligence in Medicine. 132:102386 |
| ISSN: | 0933-3657 |
| DOI: | 10.1016/j.artmed.2022.102386 |
| Popis: | Computer-aided detection systems based on deep learning have shown great potential in breast cancer detection. However, the lack of domain generalization of artificial neural networks is an important obstacle to their deployment in changing clinical environments. In this study, we explored the domain generalization of deep learning methods for mass detection in digital mammography and analyzed in-depth the sources of domain shift in a large-scale multi-center setting. To this end, we compared the performance of eight state-of-the-art detection methods, including Transformer based models, trained in a single domain and tested in five unseen domains. Moreover, a single-source mass detection training pipeline was designed to improve the domain generalization without requiring images from the new domain. The results show that our workflow generalized better than state-of-the-art transfer learning based approaches in four out of five domains while reducing the domain shift caused by the different acquisition protocols and scanner manufacturers. Subsequently, an extensive analysis was performed to identify the covariate shifts with the greatest effects on detection performance, such as those due to differences in patient age, breast density, mass size, and mass malignancy. Ultimately, this comprehensive study provides key insights and best practices for future research on domain generalization in deep learning based breast cancer detection. |
| Databáze: | OpenAIRE |
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