Learning the Correlation Between Images and Disease Labels Using Ambiguous Learning

In this paper, we present a novel approach to candidate ground truth label generation for large-scale medical image collections by combining clinically-relevant textual and visual analysis through the framework of ambiguous label learning. In particular, we present a novel string matching algorithm for extracting disease labels from patient reports associated with imaging studies. These are assigned as ambiguous labels to the images of the study. Visual analysis is then performed on the images of the study and diagnostically relevant features are extracted from relevant regions within images. Finally, we learn the correlation between the ambiguous disease labels and visual features through an ambiguous SVM learning framework. The approach was validated in a large Doppler image collection of over 7000 images showing a scalable way to semi-automatically ground truth large image collections.