EEG Correlates of Visual Recognition While Overtly Tracking a Moving Object. - Marija Uscumlic; Miriam Hägele; Benjamin Blankertz (Symbiotic, 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)

iNNvestigate Neural Networks! - Maximilian Alber; Sebastian Lapuschkin; Philipp Seegerer; Miriam Hägele; Kristof T. Schütt; Grégoire Montavon; Wojciech Samek; Klaus-Robert Müller; Sven Dähne; Pieter-Jan Kindermans (J. Mach. Learn. Res., 2019)

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)

Advancing computer vision algorithms to overcome challenges in computational pathology - Miriam Hägele (2022)

DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology. - Marco Aversa; Gabriel Nobis; Miriam Hägele; Kai Standvoss; Mihaela Chirica; Roderick Murray-Smith; Ahmed M. Alaa; Lukas Ruff; Daniela Ivanova; Wojciech Samek; Frederick Klauschen; Bruno Sanguinetti; et al. (NeurIPS, 2023)

Leveraging weak complementary labels to improve semantic segmentation of hepatocellular carcinoma and cholangiocarcinoma in H&E-stained slides. - Miriam Hägele; Johannes Eschrich; Lukas Ruff; Maximilian Alber; Simon Schallenberg; Adrien Guillot; Christoph Roderburg; Frank Tacke; Frederick Klauschen (CoRR, 2023)