Abstract
To precisely ablate moving tumours in robotic radiotherapy, time latencies have to be
compensated. In the past, several non-probabilistic regression techniques have been
investigated. Recently, relevance vector machines (RVM), a probabilistic regression technique,
have been successfully evaluated on one of the most comprehensive data sets and outperformed
six other prediction algorithms like support vector regression and wavelet based least mean
square (wLMS) [1]. The method has the distinct advantage that each predicted point is assumed
to be drawn from a normal distribution. So far only the mean of the distribution was used. We
want to investigate how the predicted variance can be used as safety feature and to construct
hybrid prediction algorithms.
compensated. In the past, several non-probabilistic regression techniques have been
investigated. Recently, relevance vector machines (RVM), a probabilistic regression technique,
have been successfully evaluated on one of the most comprehensive data sets and outperformed
six other prediction algorithms like support vector regression and wavelet based least mean
square (wLMS) [1]. The method has the distinct advantage that each predicted point is assumed
to be drawn from a normal distribution. So far only the mean of the distribution was used. We
want to investigate how the predicted variance can be used as safety feature and to construct
hybrid prediction algorithms.
| Original language | English |
|---|---|
| Pages | 59 |
| Number of pages | 1 |
| Publication status | Published - 01.06.2014 |
| Event | Proceedings of the 28th International Congress and Exhibition on Computer Assisted Radiology and Surgery - Fukuoka, Japan Duration: 25.06.2014 → 28.06.2014 |
Conference
| Conference | Proceedings of the 28th International Congress and Exhibition on Computer Assisted Radiology and Surgery |
|---|---|
| Abbreviated title | (CARS'14) |
| Country/Territory | Japan |
| City | Fukuoka |
| Period | 25.06.14 → 28.06.14 |
