Development of a clinically oriented system for melanoma diagnosis

Dermatologists have stated their preference for computer aided diagnosis (CAD) systems that provide medical justifications for the estimated diagnosis of a skin lesion. Such systems are considered to be clinically oriented in the sense that they try to detect clinical criteria and then perform a diagnosis based on that information. Unfortunately, the development of clinically inspired systems is hampered by several challenges: (i) the lack of datasets with detailed information regarding the presence and location of clinical criteria; (ii) the subtlety of some diagnostic criteria, which makes them difficult to detect; and (iii) the difficulty of using the detected criteria to predict a diagnosis. In this work, we propose a machine learning framework to address these issues. First, an image annotation approach is used to detect various medical criteria (color, texture and color structures). Information is, then, extracted from the detected criteria and a late fusion method is used to obtain a lesion diagnosis. A sensitivity of 84.6% and a specificity of 74.2% are obtained on a multi-source dataset of 804 images.