  The deployment of automated systems to diagnose diseases from medical images
is challenged by the requirement to localise the diagnosed diseases to justify
or explain the classification decision. This requirement is hard to fulfil
because most of the training sets available to develop these systems only
contain global annotations, making the localisation of diseases a weakly
supervised approach. The main methods designed for weakly supervised disease
classification and localisation rely on saliency or attention maps that are not
specifically trained for localisation, or on region proposals that can not be
refined to produce accurate detections. In this paper, we introduce a new model
that combines region proposal and saliency detection to overcome both
limitations for weakly supervised disease classification and localisation.
Using the ChestX-ray14 data set, we show that our proposed model establishes
the new state-of-the-art for weakly-supervised disease diagnosis and
localisation.
