Deep-Based Quality Assessment of Medical Images Through Domain Adaptation

Predicting the quality of multimedia content matters in many fields, but in some — diagnosis from medical imagery among them — a quality metric can directly affect decisions. We propose an efficient, shallow model that predicts medical image quality without a reference, from a small amount of annotated data.

The model is based on convolutional self-attention: it builds a complex representation from relevant local characteristics and slides over the image to interpolate a global quality score. We apply domain adaptation in both unsupervised and semi-supervised settings. Results on a dataset of images with corresponding subjective scores confirm the efficiency of the method and the value of domain adaptation for generalising perceptual quality prediction across multimedia domains.