New network topology approaches reveal differential correlation patterns in breast cancer. - Michael Bockmayr; Frederick Klauschen; Balazs Györffy; Carsten Denkert; Jan Budczies (BMC Syst. Biol., 2013)

Semiconductor sequencing: how many flows do you need? - Jan Budczies; Michael Bockmayr; Denise Treue; Frederick Klauschen; Carsten Denkert (Bioinform., 2015)

Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles. - Alexander Binder; Michael Bockmayr; Miriam Hägele; Stephan Wienert; Daniel Heim; Katharina Hellweg; Albrecht Stenzinger; Laura Parlow; Jan Budczies; Benjamin Goeppert; Denise Treue; Manato Kotani; et al. (CoRR, 2018)

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods. - Miriam Hägele; Philipp Seegerer; Sebastian Lapuschkin; Michael Bockmayr; Wojciech Samek; Frederick Klauschen; Klaus-Robert Müller; Alexander Binder (CoRR, 2019)

Interpretable Deep Neural Network to Predict Estrogen Receptor Status from Haematoxylin-Eosin Images. - Philipp Seegerer; Alexander Binder; René Saitenmacher; Michael Bockmayr; Maximilian Alber; Philipp Jurmeister; Frederick Klauschen; Klaus-Robert Müller (AI and ML for Digital Pathology, 2020)

Medulloblastoma tumor classification using deep transfer learning with multi-scale EfficientNets. - Marcel Bengs; Michael Bockmayr; Ulrich Schüller; Alexander Schlaefer (Digital Pathology, 2021)

Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning. - Marcel Bengs; Satish Pant; Michael Bockmayr; Ulrich Schüller; Alexander Schlaefer (CoRR, 2021)

Morphological and molecular breast cancer profiling through explainable machine learning. - Alexander Binder; Michael Bockmayr; Miriam Hägele; Stephan Wienert; Daniel Heim; Katharina Hellweg; Masaru Ishii; Albrecht Stenzinger; Andreas Hocke; Carsten Denkert; Klaus-Robert Müller; Frederick Klauschen (Nat. Mach. Intell., 2021)