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Interpretable Deep Neural Network to Predict Estrogen Receptor Status from Haematoxylin-Eosin Images.

, , , , , , , and . AI and ML for Digital Pathology, (2020)

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Leveraging weak complementary labels to improve semantic segmentation of hepatocellular carcinoma and cholangiocarcinoma in H&E-stained slides., , , , , , , , and . CoRR, (2023)RudolfV: A Foundation Model by Pathologists for Pathologists., , , , , , , , , and 3 other author(s). CoRR, (2024)The (Un)reliability of Saliency Methods., , , , , , , and . Explainable AI, volume 11700 of Lecture Notes in Computer Science, Springer, (2019)Interpretable Deep Neural Network to Predict Estrogen Receptor Status from Haematoxylin-Eosin Images., , , , , , , and . AI and ML for Digital Pathology, (2020)Efficient learning machines: from kernel methods to deep learning.. Technical University of Berlin, Germany, (2019)Learning how to explain neural networks: PatternNet and PatternAttribution., , , , , , and . ICLR (Poster), OpenReview.net, (2018)Balancing the composition of word embeddings across heterogenous data sets., , and . CoRR, (2020)The (Un)reliability of saliency methods., , , , , , , and . CoRR, (2017)Backprop Evolution., , , , , and . CoRR, (2018)Explanations can be manipulated and geometry is to blame., , , , , and . NeurIPS, page 13567-13578. (2019)