An expert-in-the-loop paradigm for learning medical image grouping

Abstract

Image grouping in knowledge-rich domains is challenging, since domain knowledge and expertise are key to transform image pixels into meaningful content. Manually marking and annotating images is not only labor-intensive but also ineffective. Furthermore, most traditional machine learning approaches cannot bridge this gap for the absence of experts’ input. We thus present an interactive machine learning paradigm that allows experts to become an integral part of the learning process. This paradigm is designed for automatically computing and quantifying interpretable grouping of dermatological images. In this way, the computational evolution of an image grouping model, its visualization, and expert interactions form a loop to improve image grouping. In our paradigm, dermatologists encode their domain knowledge about the medical images by grouping a small subset of images via a carefully designed interface. Our learning algorithm automatically incorporates these manually specified connections as constraints for re-organizing the whole image dataset. Performance evaluation shows that this paradigm effectively improves image grouping based on expert knowledge.