Name
Title | ||
|---|---|---|
A treatment outcome prediction model of visual field recovery using self-organizing maps. |
Abstract | ||
|---|---|---|
Brain injuries caused by stroke, trauma, or tumor often affect the visual system that leads to perceptual deficits. After intense visual stimulation of the damaged visual field or its border region, recovery may be achieved in some sectors of the visual field, but the extent of restoration is highly variable between patients and is not homogeneously distributed in the visual field. We now assess t... |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TBME.2008.2009995 | IEEE Transactions on Biomedical Engineering |
Keywords | Field | DocType |
Predictive models,Self organizing feature maps,Medical diagnostic imaging,Brain injuries,Neoplasms,Visual system,Measurement standards,Loss measurement,Feature extraction,Data mining | Computer vision,Receiver operating characteristic,Computer science,Self-organizing map,Predictive validity,Feature extraction,Artificial intelligence,Meridian (perimetry, visual field),Visual field,Perception,Visual perception,Machine learning | Journal |
Volume | Issue | ISSN |
56 | 3 | 0018-9294 |
Citations | PageRank | References |
1 | 0.34 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tobias Günther | 1 | 123 | 17.58 |
| Iris Mueller | 2 | 1 | 0.34 |
| Markus Preuss | 3 | 1 | 0.34 |
| Rudolf Kruse | 4 | 1262 | 148.56 |
| Bernhard A. Sabel | 5 | 1 | 0.68 |