Name
Title | ||
|---|---|---|
Automatic Quantification of Tumour Hypoxia From Multi-Modal Microscopy Images Using Weakly-Supervised Learning Methods. |
Abstract | ||
|---|---|---|
In recently published clinical trial results, hypoxia-modified therapies have shown to provide more positive outcomes to cancer patients, compared with standard cancer treatments. The development and validation of these hypoxia-modified therapies depend on an effective way of measuring tumor hypoxia, but a standardized measurement is currently unavailable in clinical practice. Different types of m... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TMI.2017.2677479 | IEEE Transactions on Medical Imaging |
Keywords | Field | DocType |
Tumors,Training,Medical treatment,Biomedical imaging,Manuals,Cancer,Computational modeling | Medical imaging,Supervised learning,Correlation,Artificial intelligence,Deep learning,Artificial neural network,Classifier (linguistics),Mathematics,Machine learning,Modal,Grid | Journal |
Volume | Issue | ISSN |
36 | 7 | 0278-0062 |
Citations | PageRank | References |
1 | 0.35 | 31 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gustavo Carneiro | 1 | 292 | 27.63 |
| Tingying Peng | 2 | 47 | 6.81 |
| christine bayer | 3 | 6 | 0.98 |
| Nassir Navab | 4 | 6594 | 578.60 |