Name
Title | ||
|---|---|---|
Learning From Highly Confident Samples for Automatic Knee Osteoarthritis Severity Assessment: Data From the Osteoarthritis Initiative |
Abstract | ||
|---|---|---|
Knee osteoarthritis (OA) is a chronic disease that considerably reduces patients’ quality of life. Preventive therapies require early detection and lifetime monitoring of OA progression. In the clinical environment, the severity of OA is classified by the Kellgren and Lawrence (KL) grading system, ranging from KL-0 to KL-4. Recently, deep learning methods were applied to OA severity assessment to ... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/JBHI.2021.3102090 | IEEE Journal of Biomedical and Health Informatics |
Keywords | DocType | Volume |
Training,Task analysis,Uncertainty,Osteoarthritis,Reliability,Image segmentation,Feature extraction | Journal | 26 |
Issue | ISSN | Citations |
3 | 2168-2194 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yifan Wang | 1 | 0 | 0.34 |
| Zhaori Bi | 2 | 0 | 0.34 |
| Yuxue Xie | 3 | 0 | 0.34 |
| Tao Wu | 4 | 0 | 0.34 |
| Xuan Zeng | 5 | 408 | 75.96 |
| Shuang Chen | 6 | 0 | 0.34 |
| Dian Zhou | 7 | 260 | 56.14 |