  Localization of chest pathologies in chest X-ray images is a challenging task
because of their varying sizes and appearances. We propose a novel weakly
supervised method to localize chest pathologies using class aware deep
multiscale feature learning. Our method leverages intermediate feature maps
from CNN layers at different stages of a deep network during the training of a
classification model using image level annotations of pathologies. During the
training phase, a set of \emph{layer relevance weights} are learned for each
pathology class and the CNN is optimized to perform pathology classification by
convex combination of feature maps from both shallow and deep layers using the
learned weights. During the test phase, to localize the predicted pathology,
the multiscale attention map is obtained by convex combination of class
activation maps from each stage using the \emph{layer relevance weights}
learned during the training phase. We have validated our method using 112000
X-ray images and compared with the state-of-the-art localization methods. We
experimentally demonstrate that the proposed weakly supervised method can
improve the localization performance of small pathologies such as nodule and
mass while giving comparable performance for bigger pathologies e.g.,
Cardiomegaly
