Publications

Souhrid Mukherjee, Joy D Cogan, John H Newman, John A Phillips, 3rd, Rizwan Hamid, Undiagnosed Diseases Network, Jens Meiler, und 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, Oktober 2021. [PUMA: Diseases Network; UDN; Undiagnosed clinical digenic disease disease; learning; machine oligogenic prediction; rare topic_lifescience]

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 learning; machine monitoring networks; neural rotors; structural]

Ariel Iporre-Rivas, Dorothee Saur, Karl Rohr, Gerik Scheuermann, und 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, Juli 2023. [PUMA: topic_visualcomputing graph imaging; learning; machine medical multi-modal networks; neural prediction stroke]

Mersedeh Sadeghi, Daniel Pöttgen, Patrick Ebel, und Andreas Vogelsang. Explaining the Unexplainable: The Impact of Misleading Explanations on Trust in Unreliable Predictions for Hardly Assessable Tasks. Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, 36–46, Association for Computing Machinery, New York, NY, USA, 2024. [PUMA: topic_visualcomputing XAI, explainability, learning, machine trust] URL

Leonie Lampe, Hans-Jürgen Huppertz, Sarah Anderl-Straub, Franziska Albrecht, Tommaso Ballarini, Sandrine Bisenius, Karsten Mueller, Sebastian Niehaus, Klaus Fassbender, Klaus Fliessbach, Holger Jahn, Johannes Kornhuber, Martin Lauer, Johannes Prudlo, Anja Schneider, Matthis Synofzik, Jan Kassubek, Adrian Danek, Arno Villringer, Janine Diehl-Schmid, Markus Otto, Matthias L Schroeter, und FTLD Consortium Germany. Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging. NeuroImage Clin., (37)103320:103320, Elsevier BV, Januar 2023. [PUMA: topic_neuroinspired Dementia; Diagnosis; MRI; Machine Neurodegeneration; Volumetry learning; topic_lifescience unit_test]

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: topic_lifescience cancer cancer; classification; colorectal convolutional filter; hyperspectral imaging; learning; machine median networks; post-processing; pre-processing]

Akshay Akshay, Masoud Abedi, Navid Shekarchizadeh, Fiona C Burkhard, Mitali Katoch, Alex Bigger-Allen, Rosalyn M Adam, Katia Monastyrskaya, und Ali Hashemi Gheinani. MLcps: machine learning cumulative performance score for classification problems. Gigascience, (12)Dezember 2022. [PUMA: topic_federatedlearn Python classification evaluation evaluation; learning; machine model package; problems; score unified]

Akshay Akshay, Mitali Katoch, Navid Shekarchizadeh, Masoud Abedi, Ankush Sharma, Fiona C Burkhard, Rosalyn M Adam, Katia Monastyrskaya, und Ali Hashemi Gheinani. Machine Learning Made Easy (MLme): a comprehensive toolkit for machine learning-driven data analysis. Gigascience, (13)Januar 2024. [PUMA: topic_federatedlearn AutoML; analysis; classification data learning; machine problems; visualization]