Common Thorax Diseases Recognition Using Zero-Shot Learning With Ontology in the Multi-Labeled ChestX-ray14 Data Set

Abstract

Disease detection/recognition with limited data sets and labels in the medical image domain is a very costly and greatest challenge. Although open image data sets have increased recently, researches on this problem still need to be developed. Researches to diversify data sets are both costly and face the problem of subjectivity. Unseen classes can be trained with the Zero-Shot Learning (ZSL) in order to overcome this problem. In this paper, we aimed to strengthen ZSL by using ontology as an auxiliary information for class embeddings. In our approach, ZSL is supported by the image embeddings and class embeddings of the multi-labelled ChestX-ray14 data set, as well as the semantic data from DBpedia. In this paper, which we believe will be pioneering in the medical image domain, the Cosine, Hamming and Euclidean distances were taken into account in order to maximize the similarities. We trained ResNet50 neural network with different parameters on the multi-labelled ChestX-ray14 data set. 23.25% precision value in one-to-one matching and 29.59% precision value in at least one matching were obtained. We think that this paper will make a significant contribution to the medical image domain by detecting/recognizing unseen disease images.

Keywords

• Zero-shot learning
• ResNet50
• ontology
• ChestX-ray14