English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

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

Deep multiscale convolutional feature learning for weakly supervised localization of chest pathologies in X-ray images