Learning to Localize Cross-Anatomy Landmarks in X-Ray Images with a Universal Model
Objective and Impact Statement. In this work, we develop a universal anatomical landmark detection model which learns once from multiple datasets corresponding to different anatomical regions. Compared with the conventional model trained on a single dataset, this universal model not only is more lig...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Association for the Advancement of Science (AAAS)
2022-01-01
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| Series: | BME Frontiers |
| Online Access: | http://dx.doi.org/10.34133/2022/9765095 |