  We propose a CNN based technique that aggregates feature maps from its
multiple layers that can localize abnormalities with greater details as well as
predict pathology under consideration. Existing class activation mapping (CAM)
techniques extract feature maps from either the final layer or a single
intermediate layer to create the discriminative maps and then interpolate to
upsample to the original image resolution. In this case, the subject specific
localization is coarse and is unable to capture subtle abnormalities. To
mitigate this, our method builds a novel CNN based discriminative localization
model that we call high resolution CAM (HR-CAM), which accounts for layers from
each resolution, therefore facilitating a comprehensive map that can delineate
the pathology for each subject by combining low-level, intermediate as well as
high-level features from the CNN. Moreover, our model directly provides the
discriminative map in the resolution of the original image facilitating finer
delineation of abnormalities. We demonstrate the working of our model on a
simulated abnormalities data where we illustrate how the model captures finer
details in the final discriminative maps as compared to current techniques. We
then apply this technique: (1) to classify ependymomas from grade IV
glioblastoma on T1-weighted contrast enhanced (T1-CE) MRI and (2) to predict
Parkinson's disease from neuromelanin sensitive MRI. In all these cases we
demonstrate that our model not only predicts pathologies with high accuracies,
but also creates clinically interpretable subject specific high resolution
discriminative localizations. Overall, the technique can be generalized to any
CNN and carries high relevance in a clinical setting.
