LAUSR

There has been an increasing demand for computer-aided diagnosis systems to become self-explainable. However, in fields such as dermoscopy image analysis this comes at the cost of asking physicians to annotate datasets in a detailed way, such that they simultaneously identify and manually segment regions of medical interest (dermoscopic criteria) in the images. The segmentations are then used to train an automatic detection system to reproduce the procedure. Unfortunately, providing manual segmentations is a cumbersome and time consuming task that may not be generalized to large amounts of data. Thus, this work aims to understand how much information is really needed for a system to learn to detect dermoscopic criteria. In particular, we will show that given sufficient data, it is possible to train a model to detect dermoscopic criteria solely using global annotations at the image level, and achieve similar performances to that of a fully supervised approach, where the model has access to local annotations at the pixel level (segmentations).