title: Conditional random fields and supervised learning in automated skin lesion diagnosis.
abstract: Many subproblems in automated skin lesion diagnosis (ASLD) can be unified under a single generalization of assigning a label, from an predefined set, to each pixel in an image. We first formalize this generalization and then present two probabilistic models capable of solving it. The first model is based on independent pixel labeling using maximum a-posteriori (MAP) estimation. The second model is based on conditional random fields (CRFs), where dependencies between pixels are defined using a graph structure. Furthermore, we demonstrate how supervised learning and an appropriate training set can be used to automatically determine all model parameters. We evaluate both models' ability to segment a challenging dataset consisting of 116 images and compare our results to 5 previously published methods.
authors: Department of Computing Science, Simon Fraser University, Burnaby, BC, Canada V5A 1S6.
authors: Paul
authors: P
authors: Wighton
authors: Tim K
authors: TK
authors: Lee
authors: Greg
authors: G
authors: Mori
authors: Harvey
authors: H
authors: Lui
authors: David I
authors: DI
authors: McLean
authors: M Stella
authors: MS
authors: Atkins