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Deep learning on digital mammography for expert-level diagnosis accuracy in breast cancer detection
- Source :
- Multimedia Systems. 28:1263-1274
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- Recently, computer-aided diagnosis (CAD) systems powered by deep learning (DL) algorithms have shown excellent performance in the evaluation of digital mammography for breast cancer diagnosis. However, such systems typically require pixel-level annotations by expert radiologists which is prohibitively time-consuming and expensive. Medical institutes would wonder if a high-performance breast cancer CAD system can be trained by exploring their own huge amount of historical imaging data and corresponding diagnosis reports, without additional annotations workload of their radiologists. In this study, we show that a DL classification model trained on historical mammograms with only image-level pathology labels (which can be automatically extracted from medical reports) can achieve surprisingly good diagnostic performance on newly incoming exams compared with experienced radiologists. A DL model called DenseNet was trained and cross-validated with 5979 historical exams acquired before September 2017 with biopsy-verified pathology and tested with 1194 newly obtained cases after that. For both cross-validation and test sets, the ROCs generated by DL predictions were above the ROCs generated by ratings from radiologists. For the suspicious cases which radiologists suggest biopsy (BI-RADS category 4 and 5), the DL model can reject 60% of false biopsies on benign breasts while keeping 95% sensitivity. For the mammograms based on which radiologists were not able to make a diagnosis (BI-RADS 0), the DL model still achieved an AUC score of 79%. Moreover, the model is able to localize lesions on mammograms although such information was not provided in the training phase. Finally, the impact of input image resolution and different DL model architectures on the diagnostic accuracy were also presented and analyzed.
- Subjects :
- medicine.medical_specialty
Digital mammography
Computer Networks and Communications
Computer science
education
CAD
02 engineering and technology
Imaging data
Breast cancer
0202 electrical engineering, electronic engineering, information engineering
Media Technology
medicine
Medical physics
business.industry
Deep learning
020207 software engineering
Workload
medicine.disease
Cad system
Hardware and Architecture
Training phase
020201 artificial intelligence & image processing
Artificial intelligence
business
Software
Information Systems
Subjects
Details
- ISSN :
- 14321882 and 09424962
- Volume :
- 28
- Database :
- OpenAIRE
- Journal :
- Multimedia Systems
- Accession number :
- edsair.doi...........fc3cd5d441d003a0f8f81034a7e1de44