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Conditional Random Fields and Supervised Learning in Automated Skin Lesion Diagnosis

Resource type
Date created
2011
Authors/Contributors
Author: Lee, Tim
Author: Mori, Greg
Author: Lui, Harvey
Abstract
Many subproblems in automated skin lesion diagnosis (ASLD) canbe unified under a single generalization of assigning a label, from an predefinedset, to each pixel in an image. We first formalize this generalizationand then present two probabilistic models capable of solving it. The firstmodel is based on independent pixel labeling using maximum a-posteriori(MAP) estimation. The second model is based on conditional randomfields (CRFs), where dependencies between pixels are defined using agraph structure. Furthermore, we demonstrate how supervised learningand an appropriate training set can be used to automatically determineall model parameters. We evaluate both models' ability to segment achallenging dataset consisting of 116 images and compare our results to5 previously published methods.
Document
Published as
International Journal of Biomedical Imaging
Volume 2011 (2011), Article ID 846312, 10 pages
http://dx.doi.org/10.1155/2011/846312
Publication title
International Journal of Biomedical Imaging
Document title
Conditional Random Fields and Supervised Learning in Automated Skin Lesion Diagnosis
Date
2011
Volume
2011
Publisher DOI
10.1155/2011/846312
Copyright statement
Copyright is held by the author(s).
Permissions
You are free to copy, distribute and transmit this work under the following conditions: You must give attribution to the work (but not in any way that suggests that the author endorses you or your use of the work); You may not use this work for commercial purposes.
Scholarly level
Peer reviewed?
Yes
Language
English
Member of collection
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846312.pdf 7.56 MB

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