CELNet: Evidence Localization for Pathology Images using Weakly Supervised Learning

16 Sep 2019Yongxiang HuangAlbert C. S. Chung

Despite deep convolutional neural networks boost the performance of image classification and segmentation in digital pathology analysis, they are usually weak in interpretability for clinical applications or require heavy annotations to achieve object localization. To overcome this problem, we propose a weakly supervised learning-based approach that can effectively learn to localize the discriminative evidence for a diagnostic label from weakly labeled training data... (read more)

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