Title |
Classification of Structural MRI Images in Alzheimer's Disease from the Perspective of Ill-Posed Problems
|
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Published in |
PLOS ONE, October 2012
|
DOI | 10.1371/journal.pone.0044877 |
Pubmed ID | |
Authors |
Ramon Casanova, Fang-Chi Hsu, for the Alzheimer's Disease Neuroimaging Initiative Mark A. Espeland |
Abstract |
Machine learning neuroimaging researchers have often relied on regularization techniques when classifying MRI images. Although these were originally introduced to deal with "ill-posed" problems it is rare to find studies that evaluate the ill-posedness of MRI image classification problems. In addition, to avoid the effects of the "curse of dimensionality" very often dimension reduction is applied to the data. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
Egypt | 2 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 3% |
Unknown | 76 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 14 | 18% |
Student > Master | 14 | 18% |
Student > Ph. D. Student | 12 | 15% |
Student > Bachelor | 6 | 8% |
Other | 5 | 6% |
Other | 13 | 17% |
Unknown | 14 | 18% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 12 | 15% |
Psychology | 9 | 12% |
Computer Science | 9 | 12% |
Engineering | 8 | 10% |
Agricultural and Biological Sciences | 6 | 8% |
Other | 18 | 23% |
Unknown | 16 | 21% |