Chapter title |
A Sparse Bayesian Learning Algorithm for Longitudinal Image Data.
|
---|---|
Chapter number | 49 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015
|
Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, January 2015
|
DOI | 10.1007/978-3-319-24574-4_49 |
Pubmed ID | |
Book ISBNs |
978-3-31-924573-7, 978-3-31-924574-4
|
Authors |
Mert R. Sabuncu |
Editors |
Nassir Navab, Joachim Hornegger, William M. Wells, Alejandro F. Frangi |
Abstract |
Longitudinal imaging studies, where serial (multiple) scans are collected on each individual, are becoming increasingly widespread. The field of machine learning has in general neglected the longitudinal design, since many algorithms are built on the assumption that each datapoint is an independent sample. Thus, the application of general purpose machine learning tools to longitudinal image data can be sub-optimal. Here, we present a novel machine learning algorithm designed to handle longitudinal image datasets. Our approach builds on a sparse Bayesian image-based prediction algorithm. Our empirical results demonstrate that the proposed method can offer a significant boost in prediction performance with longitudinal clinical data. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 13 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 3 | 23% |
Student > Master | 2 | 15% |
Student > Bachelor | 2 | 15% |
Lecturer | 1 | 8% |
Student > Doctoral Student | 1 | 8% |
Other | 2 | 15% |
Unknown | 2 | 15% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 2 | 15% |
Computer Science | 2 | 15% |
Mathematics | 1 | 8% |
Economics, Econometrics and Finance | 1 | 8% |
Earth and Planetary Sciences | 1 | 8% |
Other | 1 | 8% |
Unknown | 5 | 38% |