Publications

Joel Jonsson, Bevan Leslie Cheeseman, und Ivo Sbalzarini. APR-CNN: Convolutional Neural Networks for the Adaptive Particle Representation of Large Microscopy Images. Transactions on Machine Learning Research, 2025. [PUMA: convolutional images microscopy networks neural yaff] URL

Maksim Kukushkin, Martin Bogdan, und Thomas Schmid. BiCAE -- A Bimodal Convolutional Autoencoder for Seed Purity Testing. In Albert Bifet, Tomas Krilavicius, Ioanna Miliou, und Slawomir Nowaczyk (Hrsg.), Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, 447--462, Springer Nature Switzerland, Cham, 2024. [PUMA: Autoencoder BiCAE Bimodal Convolutional Purity Seed Testing zno]

Aris Marcolongo, Mykhailo Vladymyrov, Sebastian Lienert, Nadav Peleg, Sigve Haug, und Jakob Zscheischler. Predicting years with extremely low gross primary production from daily weather data using Convolutional Neural Networks. Environmental Data Science, (1):e2, 2022. [PUMA: Convolutional Predicting data gross low primary production weather zno networks neural]

Johannes Gerritzen, Andreas Hornig, Peter Winkler, und Maik Gude. Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning. ECCM21 - Proceedings of the 21st European Conference on Composite Materials, (3):1252--1259, European Society for Composite Materials (ESCM), 02.07.2024. [PUMA: Convolutional Direct FIS_scads Fiber Strain area_architectures dependency, identification, networks, neural parameter plastics rate reinforced topic_engineering yaff machine learning] URL

David Nam, Julius Chapiro, Valerie Paradis, Tobias Paul Seraphin, und Jakob Nikolas Kather. Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction. JHEP Rep., (4)4:100443, Elsevier BV, April 2022. [PUMA: AI CNN Communications DICOM Diagnosis Digital HCC Imaging Individual ML MVI Medicine NAFLD NASH Prognosis Reporting TACE TRIPOD Transparent WSIs a and artificial carcinoma chemoembolisation convolutional data deep diagnostic disease fatty for hepatocellular images imaging in integration intelligence invasion learning liver machine microvascular model multimodal multivariable network neural non-alcoholic of or prediction slide steatohepatitis support system topic_lifescience transarterial whole zno]

Lucas Schneider, Sara Laiouar-Pedari, Sara Kuntz, Eva Krieghoff-Henning, Achim Hekler, Jakob N Kather, Timo Gaiser, Stefan Fröhling, und Titus J Brinker. Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review. Eur. J. Cancer, (160):80--91, Elsevier BV, Januar 2022. [PUMA: Biomarker Cancer Convolutional Multimodal Omics fusion identification networks neural topic_lifescience zno]

Narmin Ghaffari Laleh, Hannah Sophie Muti, Chiara Maria Lavinia Loeffler, Amelie Echle, Oliver Lester Saldanha, Faisal Mahmood, Ming Y Lu, Christian Trautwein, Rupert Langer, Bastian Dislich, Roman D Buelow, Heike Irmgard Grabsch, Hermann Brenner, Jenny Chang-Claude, Elizabeth Alwers, Titus J Brinker, Firas Khader, Daniel Truhn, Nadine T Gaisa, Peter Boor, Michael Hoffmeister, Volkmar Schulz, und Jakob Nikolas Kather. Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology. Med. Image Anal., (79)102474:102474, Elsevier BV, Juli 2022. [PUMA: Computational Convolutional Learning; Multiple-Instance Vision Weakly-supervised deep learning neural topic_lifescience transformers; zno artificial intelligence pathology networks]

Veronika Scholz, Peter Winkler, Andreas Hornig, Maik Gude, und Angelos Filippatos. Structural damage identification of composite rotors based on fully connected neural networks and convolutional neural networks. Sensors (Basel), (21)6:2005, MDPI AG, März 2021. [PUMA: (SHM) composite composites; connected convolutional dense fully health machine monitoring neural rotors; structural xack learning networks]

Mariia Tkachenko, Claire Chalopin, Boris Jansen-Winkeln, Thomas Neumuth, Ines Gockel, und Marianne Maktabi. Impact of pre- and post-processing steps for supervised classification of colorectal cancer in hyperspectral images. Cancers (Basel), (15)7April 2023. [PUMA: cancer classification colorectal convolutional filter hyperspectral imaging learning machine median networks post-processing pre-processing topic_lifescience yaff]