AMAT-Net: An Unbiased Network with High Performance for Metabolic Diseases Prediction Using Facial Images

Abstract

Recent studies have found that human faces not only encode personal identity information, but also related to plenty of human health information. However, for health status prediction, bias remains a challenge which may be introduced by the intrinsic dependency between variables. For example, demographic variables such as gender or age are associated to many diseases, causing model learn to infer the age or gender rather than the real pathological cues. In order to eliminate the bias and guarantee the high performance of health prediction from facial images, we propose a novel unbiased model based on attention mechanism and adversarial technique, named AMAT-Net for health prediction. More specifically, we minimize the target prediction loss and maximize the bias variable prediction loss during training, to encourage extracted features to keep predictive ability while decouple with the bias. At the same time, the tansformer module is introduced to adaptively extract the features of different tasks. We constructed a dataset composed of facial images with demographic information and disease labels. The experimental results demonstrate that our model not only shows superior performance on metabolic disease prediction but also largely reduce the prediction bias on different age groups.

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