Name
Abstract | ||
|---|---|---|
To enhance the quality of economically efficient healthcare, we propose a preventive planning service for next-generation screening based on a longitudinal prediction. This newly proposed framework may bring important advancements in prevention by identifying the early stages of cancer, which will help in further diagnoses and initial treatment planning. The preventive service may also solve the o... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TII.2016.2595399 | IEEE Transactions on Industrial Informatics |
Keywords | Field | DocType |
Kernel,Cancer,Colonography,Feature extraction,Informatics,Computed tomography,Design automation | Kernel (linear algebra),Data mining,Informatics,Anomaly detection,Computer science,Radiation treatment planning,Feature extraction,Electronic design automation,Artificial intelligence,Medical diagnosis,Machine learning,Cancer staging | Journal |
Volume | Issue | ISSN |
12 | 6 | 1551-3203 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuichi Motai | 1 | 230 | 24.68 |
| Dingkun Ma | 2 | 0 | 0.34 |
| Hiroyuki Yoshida | 3 | 69 | 17.19 |