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Philipp Seegerer
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2020 – today
- 2021
- [j3]Seul-Ki Yeom, Philipp Seegerer
, Sebastian Lapuschkin
Pruning by explaining: A novel criterion for deep neural network pruning. Pattern Recognit. 115: 107899 (2021) - 2020
- [p1]Philipp Seegerer
Interpretable Deep Neural Network to Predict Estrogen Receptor Status from Haematoxylin-Eosin Images. AI and ML for Digital Pathology 2020: 16-37
2010 – 2019
- 2019
- [j2]Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, Pieter-Jan Kindermans:
iNNvestigate Neural Networks! J. Mach. Learn. Res. 20: 93:1-93:8 (2019) - [i3]Miriam Hägele, Philipp Seegerer
Resolving challenges in deep learning-based analyses of histopathological images using explanation methods. CoRR abs/1908.06943 (2019) - [i2]Seul-Ki Yeom, Philipp Seegerer, Sebastian Lapuschkin
Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning. CoRR abs/1912.08881 (2019) - 2018
- [i1]Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, Pieter-Jan Kindermans:
iNNvestigate neural networks! CoRR abs/1808.04260 (2018) - 2014
- [j1]Oliver Zettinig, Tommaso Mansi, Dominik Neumann, Bogdan Georgescu, Saikiran Rapaka
Data-driven estimation of cardiac electrical diffusivity from 12-lead ECG signals. Medical Image Anal. 18(8): 1361-1376 (2014) - [c1]Philipp Seegerer
Estimation of Regional Electrical Properties of the Heart from 12-Lead ECG and Images. STACOM 2014: 204-212
Coauthor Index
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