Baldassarre, L;
Barla, A;
Gianesin, B;
Marinelli, M;
(2008)
Vector Valued Regression for Iron Overload Estimation.
In:
19th International Conference on Pattern Recognition (ICPR 2008).
(pp. pp. 2243-2246).
IEEE: Piscataway, US.
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Abstract
In this work we present and discuss in detail a novel vector-valued regression technique: our approach allows for an all-at-once estimation, as opposed to solve a number of scalar-valued regression tasks. Despite its general purpose nature, the method has been designed to solve a delicate medical issue: a reliable and noninvasive assessment of body-iron overload.The Magnetic Iron Detector (MID) measures the magnetic track of a person, which depends on the anthropometric characteristics and the body-iron burden. We aim to provide an estimate of this signal in absence of iron overload. We show how this question can be formulated as the estimation of a vector-valued function which encompasses the prior knowledge on the shape of the magnetic track. This is accomplished by designing an appropriate vector-valued feature map. We successfully applied the method on a dataset of 84 volunteers.
| Type: | Proceedings paper |
|---|---|
| Title: | Vector Valued Regression for Iron Overload Estimation |
| ISBN-13: | 9781424421749 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICPR.2008.4761759 |
| Publisher version: | http://dx.doi.org/10.1109/ICPR.2008.4761759 |
| Language: | English |
| Additional information: | © 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/144214 |
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