Detecting Visually Observable Disease Symptoms from Faces.
Recent years have witnessed an increasing interest in the application of machine learning to clinical informatics and healthcare systems. A significant amount of research has been done on healthcare systems based on supervised learning. In this study, we present a generalized solution to detect visually observable symptoms on faces using semi-supervised anomaly detection combined with machine vision algorithms. We rely on the disease-related statistical facts to detect abnormalities and classify them into multiple categories to narrow down the possible medical reasons of detecting. Our method is in contrast with most existing approaches, which are limited by the availability of labeled training data required for supervised learning, and therefore offers the major advantage of flagging any unusual and visually observable symptoms.
University of Rochester, Rochester, USA.
Kuan
K
Wang
University of Rochester, Rochester, USA.
Jiebo
J
Luo
Anomaly detection; Classification; Clinical informatics; Computer vision; Imbalanced dataset; Semi-supervised Learning
J Exp Psychol Gen. 2013 Nov;142(4):1323-34
24246059
Elife. 2014 Jun 24;3:e02020
24963138