Breast mass classification based on BIRADs multi-task learning model(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2023年第10期
- Page:
- 1220-1227
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- Breast mass classification based on BIRADs multi-task learning model
- Author(s):
- WU Shuyu1; ZHOU Lu1; WANG Linjing1; LI Huijun1; ZHANG Shuxu1; MEI Yingjie2
- 1. Department of Radiotherapy, Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou 510095, China 2. Department of Radiology, Guangdong Provincial Peoples Hospital, Guangzhou 510080, China
- Keywords:
- Keywords: breast cancer mass classification data-driven transfer learning multi-task learning
- PACS:
- R318；R445
- DOI:
- DOI:10.3969/j.issn.1005-202X.2023.10.005
- Abstract:
- Abstract: Objective To propose a breast mass classification method based on multi-task learning model of breast imaging reporting and data system (BIRADs) for addressing the challenges in breast mass classification and deep learning applications. Methods A BIRADs multi-task learning model with a transfer learning-based morphological feature extractor, texture feature extractor, and multi-task classifier, which enables the assessment of BIRADs-related characteristics such as margin, shape, density and subtlety, was constructed. Model performance was analyzed through experiments involving training strategies, network architectures, and input types. Results With transfer learning as the training strategy, both Base and BIRADs models exhibit significant performance improvement. The models with original mass images as input outperformed those with masked images. With transfer learning as the training strategy and original mass images as input, the BIRADs model had higher AUC, accuracy, precision, recall rate, and F1-score as compared with Base model (0.830 vs 0.793, 0.747±0.024 vs 0.712±0.023, 0.643±0.032 vs 0.607±0.030, 0.774±0.037 vs 0.715±0.042, 0.702±0.028 vs 0.656±0.029, respectively). The multi-task learning model demonstrated significant advantages in breast mass classification. Conclusion The proposed BIRADs multi-task learning model combining clinical knowledge and data-driven methods enhances accuracy and robustness in breast mass classification, which is expected to improve the diagnostic accuracy of breast cancer.
Last Update: 2023-10-27