Name
Abstract | ||
|---|---|---|
•A conceptual framework to synergistically marry the unmatched strengths of high-level human knowledge (natural intelligence) and artificial intelligence to arrive at a robust, accurate, and general object recognition method for medical image analysis.•The AAR-DL approach combines an advanced anatomy-modeling strategy (AAR), model-based object recognition (AAR-R), and deep learning object detection networks.•AAR-DL consists of 4 key modules wherein prior knowledge is made use of judiciously at every stage.•AAR-DL has demonstrated high accuracy and robustness to image artifacts and deviations.•AAR-DL performs like an expert human operator in object recognition with localization accuracy within 1–2 voxels and remarkable robustness. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.media.2022.102527 | Medical Image Analysis |
Keywords | DocType | Volume |
Organ recognition,Deep learning,Automatic anatomy recognition,Anatomic models,Natural intelligence,Artificial intelligence | Journal | 81 |
ISSN | Citations | PageRank |
1361-8415 | 0 | 0.34 |
References | Authors | |
0 | 15 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao Jin | 1 | 0 | 0.34 |
| Jayaram K. Udupa | 2 | 2481 | 322.29 |
| Liming Zhao | 3 | 0 | 0.34 |
| Yubing Tong | 4 | 0 | 0.34 |
| Dewey Odhner | 5 | 0 | 0.34 |
| Gargi Pednekar | 6 | 0 | 0.34 |
| Sanghita Nag | 7 | 0 | 0.34 |
| Sharon Lewis | 8 | 0 | 0.34 |
| Nicholas Poole | 9 | 0 | 0.34 |
| Sutirth Mannikeri | 10 | 0 | 0.34 |
| Sudarshana Govindasamy | 11 | 0 | 0.34 |
| Aarushi Singh | 12 | 0 | 0.34 |
| Joe Camaratta | 13 | 0 | 0.34 |
| Steve Owens | 14 | 0 | 0.34 |
| Drew A Torigian | 15 | 0 | 0.34 |