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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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BatchNorm Decomposition for Deep Neural Network Interpretation., and . IWANN (2), volume 11507 of Lecture Notes in Computer Science, page 280-291. Springer, (2019)Interpretable Deep Neural Network to Predict Estrogen Receptor Status from Haematoxylin-Eosin Images., , , , , , , and . AI and ML for Digital Pathology, (2020)Explanation-Guided Training for Cross-Domain Few-Shot Classification., , , , , and . ICPR, page 7609-7616. IEEE, (2020)Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network Attribution., , , , and . ICPR, page 4949-4956. IEEE, (2020)The Joint Submission of the TU Berlin and Fraunhofer FIRST (TUBFI) to the ImageCLEF2011 Photo Annotation Task, , , , , and . CLEF (Notebook Papers/Labs/Workshop), (2011)Adversarial Attacks on Remote User Authentication Using Behavioural Mouse Dynamics., , , , and . IJCNN, page 1-10. IEEE, (2019)Detection of Adversarial Supports in Few-shot Classifiers Using Feature Preserving Autoencoders and Self-Similarity., , , , and . CoRR, (2020)On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy., , , , , and . CoRR, (2021)When brain and behavior disagree: Tackling systematic label noise in EEG data with machine learning., , , , , , and . BCI, page 1-4. IEEE, (2014)Deep One-Class Classification, , , , , , , and . Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, page 4393--4402. Stockholmsmässan, Stockholm Sweden, PMLR, (10--15 Jul 2018)