Severity grading of psoriatic plaques using deep CNN based multi-task learning

This paper addresses the problem of automatic machine analysis based severity scoring of psoriasis skin disease. Three different disease parameters namely, erythema, scaling and induration are considered for such severity grading. Given an image containing a psoriatic plaque the task is to predict severity scores for all the three parameters. This paper presents a novel deep CNN based architecture for achieving the task. Apart from viewing this task as three different single task learning (STL) problems (i.e. three different classification problems), a new multi-task learning (MTL) is also presented where the three classification tasks are treated as interdependent and thereby the neural net is trained accordingly. A new annotated dataset consisting of seven hundred and seven (707) images has been constructed on which the performance of the severity scoring algorithms have been reported. Several competing baselines are considered to compare the performance of STL and MTL approaches. Experimental result shows that the deep CNN based architectures (both the STL and MTL) achieve promising performances, MTL producing slightly superior results to that of STL.