Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions

Abstract. Diagnostic imaging often requires the simultaneous identification
of a multitude of findings of varied size and appearance. Beyond
global indication of said findings, the prediction and display of localization
information improves trust in and understanding of results when
augmenting clinical workflow. Medical training data rarely includes more
than global image-level labels as segmentations are time-consuming and
expensive to collect. We introduce an approach to managing these practical
constraints by applying a novel architecture which learns at multiple
resolutions while generating saliency maps with weak supervision……..

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Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions

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