Publications

Oliver Kirsten, Martin Bogdan, and Sophie Adama. Evaluating the DoC-Forest tool for Classifying the State of Consciousness in a Completely Locked-In Syndrome Patient. 2023 7th International Conference on Imaging, Signal Processing and Communications (ICISPC), 37-41, 2023. [PUMA: Complexity Computational Consciousness Information Locked-In Measures Modeling Neuroscience Prediction Predictive Processing Signal Syndrome Theory Training algorithms and data learning modeling models processing zno machine]

Dimitra Kiakou, Adam Adamopoulos, and Nico Scherf. Graph-Based Disease Prediction in Neuroimaging: Investigating the Impact of Feature Selection. Worldwide Congress on “Genetics, Geriatrics and Neurodegenerative Diseases Research", 223--230, 2022. [PUMA: Disease Feature Graph-Based Impact Investigating Neuroimaging Prediction Selection learning topic_neuroinspired yaff]

Lummy Maria Monteiro, Joao Saraiva, Rodolfo Toscan, Peter Stadler, Rafael Silva-Rocha, and Ulisses Nunes da Rocha. PredicTF: prediction of bacterial transcription factors in complex microbial communities using deep learning. Environmental Microbiome, (17)December 2022. [PUMA: PredicTF: bacterial complex learning microbialdeep prediction transcription zno]

Souhrid Mukherjee, Joy D Cogan, John H Newman, John A Phillips, 3rd, Rizwan Hamid, Undiagnosed Diseases Network, Jens Meiler, and John A Capra. Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network. Am. J. Hum. Genet., (108)10:1946--1963, Elsevier BV, October 2021. [PUMA: Diseases Network UDN Undiagnosed_clinical_digenic disease learning machine oligogenic prediction rare topic_lifescience zno]

David Nam, Julius Chapiro, Valerie Paradis, Tobias Paul Seraphin, and 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]

Scarlet Brockmoeller, Amelie Echle, Narmin Ghaffari Laleh, Susanne Eiholm, Marie Louise Malmstrøm, Tine Plato Kuhlmann, Katarina Levic, Heike Irmgard Grabsch, Nicholas P West, Oliver Lester Saldanha, Katerina Kouvidi, Aurora Bono, Lara R Heij, Titus J Brinker, Ismayil Gögenür, Philip Quirke, and Jakob Nikolas Kather. Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer. J. Pathol., (256)3:269--281, Wiley, March 2022. [PUMA: AI; LNM adipose and artificial bowel colorectal deep digital early inflamed metastasis; new pT1 pT2 prediction predictive tissue; topic_lifescience zno learning intelligence pathology biomarker cancer]

Ariel Iporre-Rivas, Dorothee Saur, Karl Rohr, Gerik Scheuermann, and Christina Gillmann. Stroke-GFCN: ischemic stroke lesion prediction with a fully convolutional graph network. J. Med. Imaging (Bellingham), (10)4:044502, SPIE-Intl Soc Optical Eng, July 2023. [PUMA: graph imaging learning machine medical multi-modal networks neural prediction stroke topic_visualcomputing yaff]