  In this work, we exploit the task of joint classification and weakly
supervised localization of thoracic diseases from chest radiographs, with only
image-level disease labels coupled with disease severity-level (DSL)
information of a subset. A convolutional neural network (CNN) based
attention-guided curriculum learning (AGCL) framework is presented, which
leverages the severity-level attributes mined from radiology reports. Images in
order of difficulty (grouped by different severity-levels) are fed to CNN to
boost the learning gradually. In addition, highly confident samples (measured
by classification probabilities) and their corresponding class-conditional
heatmaps (generated by the CNN) are extracted and further fed into the AGCL
framework to guide the learning of more distinctive convolutional features in
the next iteration. A two-path network architecture is designed to regress the
heatmaps from selected seed samples in addition to the original classification
task. The joint learning scheme can improve the classification and localization
performance along with more seed samples for the next iteration. We demonstrate
the effectiveness of this iterative refinement framework via extensive
experimental evaluations on the publicly available ChestXray14 dataset. AGCL
achieves over 5.7\% (averaged over 14 diseases) increase in classification AUC
and 7%/11% increases in Recall/Precision for the localization task compared to
the state of the art.
