Conditional random fields and supervised learning in automated skin lesion diagnosis
Journal of Biomedical Imaging - Special issue on Machine Learning in Medical Imaging
Methodological review: Computerized analysis of pigmented skin lesions: A review
Artificial Intelligence in Medicine
A two-stage approach for discriminating melanocytic skin lesions using standard cameras
Expert Systems with Applications: An International Journal
Automatic skin lesion segmentation based on texture analysis and supervised learning
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Computers in Biology and Medicine
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We present a general model using supervised learning and MAP estimation that is capable of performing many common tasks in automated skin lesion diagnosis. We apply our model to segment skin lesions, detect occluding hair, and identify the dermoscopic structure pigment network. Quantitative results are presented for segmentation and hair detection and are competitive when compared to other specialized methods. Additionally, we leverage the probabilistic nature of the model to produce receiver operating characteristic curves, show compelling visualizations of pigment networks, and provide confidence intervals on segmentations.