Deep Learning in Breast Cancer Screening
| Autor: | Hugh Harvey, Galvin Khara, Andreas Heindl, Joseph Yearsley, Edith Karpati, Gabor Forrai, Peter Kecskemethy, Michael O’Neill, Tobias Rijken, Dimitrios Korkinof |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
medicine.diagnostic_test
Recall Computer science business.industry Deep learning education Machine learning computer.software_genre medicine.disease Computer aided detection Breast cancer screening Workflow Breast cancer medicine Screening programs Mammography Artificial intelligence business computer |
| Zdroj: | Artificial Intelligence in Medical Imaging ISBN: 9783319948775 |
| DOI: | 10.1007/978-3-319-94878-2_14 |
| Popis: | Traditional computer aided detection (CAD) systems for breast cancer screening relied on machine learning with human-coded feature-engineering. They have largely failed to fulfill the promise of improving screening accuracy and workflow efficiency, and are often associated with increased recall rates and avoidable screening costs due to high instances of false positive markings. Advances in machine learning (such as deep learning) are on the cusp of providing more effective, more efficient, and even more patient-centric breast cancer screening support than ever before. By leveraging the consistent high sensitivity and specificity performance of autonomous systems, in combination with expert human oversight, the potential for efficient single-reader software-supported screening programs with low recall rates is on the horizon. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|