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DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows


May 30, 2021
Samuel von Baußnern, Johannes Otterbach, Adrian Loy, Mathieu Salzmann, Thomas Wollmann

* 14 pages, 4 figures, 4 tables 

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Chameleon: A Semi-AutoML framework targeting quick and scalable development and deployment of production-ready ML systems for SMEs


May 08, 2021
Johannes Otterbach, Thomas Wollmann


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Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge


Jul 22, 2018
Mitko Veta, Yujing J. Heng, Nikolas Stathonikos, Babak Ehteshami Bejnordi, Francisco Beca, Thomas Wollmann, Karl Rohr, Manan A. Shah, Dayong Wang, Mikael Rousson, Martin Hedlund, David Tellez, Francesco Ciompi, Erwan Zerhouni, David Lanyi, Matheus Viana, Vassili Kovalev, Vitali Liauchuk, Hady Ahmady Phoulady, Talha Qaiser, Simon Graham, Nasir Rajpoot, Erik Sjöblom, Jesper Molin, Kyunghyun Paeng, Sangheum Hwang, Sunggyun Park, Zhipeng Jia, Eric I-Chao Chang, Yan Xu, Andrew H. Beck, Paul J. van Diest, Josien P. W. Pluim

* Overview paper of the TUPAC16 challenge: http://tupac.tue-image.nl/ 

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Automatic breast cancer grading in lymph nodes using a deep neural network


Jul 24, 2017
Thomas Wollmann, Karl Rohr


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