Publications

Jan-Niklas Eckardt, Waldemar Hahn, Christoph Röllig, Sebastian Stasik, Uwe Platzbecker, Carsten Müller-Tidow, Hubert Serve, Claudia D Baldus, Christoph Schliemann, Kerstin Schäfer-Eckart, Maher Hanoun, Martin Kaufmann, Andreas Burchert, Christian Thiede, Johannes Schetelig, Martin Sedlmayr, Martin Bornhäuser, Markus Wolfien, and Jan Moritz Middeke. Mimicking clinical trials with synthetic acute myeloid leukemia patients using generative artificial intelligence. NPJ Digit. Med., (7)1:76, March 2024. [PUMA: Yaff ai artificial clinical generative intelligence leukemia myeloid synthetic trials]

Logics in Artificial Intelligence: 18th European Conference, JELIA 2023, Dresden, Germany, September 20--22, 2023, Proceedings. In Sarah Gaggl, Maria Vanina Martinez, and Magdalena Ortiz (Eds.), Lecture notes in computer science, Springer Nature Switzerland, Cham, 2023. [PUMA: AI Artificial Intelligence Logics in]

Andre de Carvalho, Robson Bonidia, Jude Dzevela Kong, Mariana Dauhajre, Claudio Struchiner, Guilherme Goedert, Peter F. Stadler, Maria Emilia Walter, Danilo Sanches, Troy Day, Marcia Castro, John Edmunds, Manuel Colome-Hidalgo, Demian Arturo Herrera Morban, Edian F. Franco, Cesar Ugarte-Gil, Patricia Espinoza-Lopez, Gabriel Carrasco-Escobar, and Ulisses Rocha. Democratising Artificial Intelligence for Pandemic Preparedness and Global Governance in Latin American and Caribbean Countries. arXiv, 2024. [PUMA: Artificial Computer FOS Intelligence Zno and information sciences] URL

Ekaterina Borisova, Raia Abu Ahmad, Georg Rehm, Ricardo Usbeck, Jennifer D’Souza, Markus Stocker, Sören Auer, Judith Gilsbach, Anastasia Wolschewski, Johannes Keller, Daniel Schneider, Thomas Neumuth, and Sonja Schimmler. NFDI4DS Transfer and Application. Gesellschaft für Informatik e.V., 2023. [PUMA: Artificial Data Infrastructures Intelligence NFDI NFDI4DS Research Science Zno] URL

Julián Méndez, Marc Satkowski, and Rufat Rzayev. How Does Explainability Look in Hybrid User Interfaces?. 251--256, Oct 16, 2023. [PUMA: topic_visualcomputing FIS_scads artificial explainable hybrid intelligence, interfaces, mixed reality user] URL

Sarah Perez, Suryanarayana Maddu, Ivo F. Sbalzarini, and Philippe Poncet. Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems. Journal of computational physics, (491)Academic Press Inc., Oct 15, 2023. [PUMA: topic_lifescience Adaptive Artificial Bayesian Carlo, FIS_scads Hamiltonian Intelligence, Monte Multi-objective Quantification Uncertainty learning, networks, neural physics-informed training, weight]

Neringa Jurenaite, Daniel León-Periñán, Veronika Donath, Sunna Torge, and René Jäkel. SetQuence & SetOmic: Deep set transformers for whole genome and exome tumour analysis. BioSystems, (235)Elsevier, January 2024. [PUMA: topic_federatedlearn livinglab Artificial Biomedical Exome/genetics, FIS_scads Humans, Intelligence, Medical Neoplasms/genetics Oncology, Research,]

Erik Marx, Clemens Witt, and Thiemo Leonhardt. Identifying Secondary School Students' Misconceptions about Machine Learning: An Interview Study. WiPSCE '24: Proceedings of the 19th WiPSCE Conference on Primary and Secondary Computing Education Research, 1--10, Association for Computing Machinery, Sep 16, 2024. [PUMA: area_responsibleai FIS_scads artificial conceptions intelligence, interview learning, machine mental misconceptions, models, qualitative research, students study,]

Erik Marx, Thiemo Leonhardt, and Nadine Bergner. Brief Summary of Existing Research on Students’ Conceptions of AI. 1--2, October 2022. [PUMA: area_responsibleai FIS_scads artificial beliefs, conceptions, education, ideas, intelligence, k-12 learning, machine mental models, preconceptions]

Erik Marx, Thiemo Leonhardt, and Nadine Bergner. Secondary school students' mental models and attitudes regarding artificial intelligence - A scoping review. Computers and education: artificial intelligence, (5)5:1--13, Elsevier Science B.V., January 2023. [PUMA: area_responsibleai Artificial Attitudes, Conceptions, FIS_scads K-12 Mental Scoping education, intelligence, models, review]

Ravi Pradip, and Frank Cichos. Deep reinforcement learning with artificial microswimmers. Emerging Topics in Artificial Intelligence (ETAI) 2022, (12204):104--110, 2022. [PUMA: topic_physchemistry Deep artificial learning microswimmers reinforcement]

Jana Riedel, and Julia Kleppsch. Wie bereit sind Studierende für die Nutzung von KI-Technologien? Eine Annäherung an die KI-Readiness Studierender im Kontext des Projektes "tech4comp". Waxmann : Münster ; New York, 2021. [PUMA: (Learning 370 Activities), Artificial Assessment, Bewertung, Bildungswesen, Deployment Digitale Education, Empirical Empirische Erziehung, Forschung, Higher Hochschule, Hochschullehre, Human Intelligenz, Judgement, Judgment, K\"{u}nstliche Learning Lernprozess, Male Medien, Medieneinsatz, Mediennutzung, Mensch, Nachteil, Project, Projects Projekt, Qualitative Schul- Student, Technologie, Technology, University Untersuchung, Use Utilisation Utilization Vergleich, Vorteil, being, education institute, intelligence, lecturing, media, of process, research student, study, teaching, und] URL

Qinghe Zeng, Christophe Klein, Stefano Caruso, Pascale Maille, Narmin Ghaffari Laleh, Daniele Sommacale, Alexis Laurent, Giuliana Amaddeo, David Gentien, Audrey Rapinat, Hélène Regnault, Cécile Charpy, Cong Trung Nguyen, Christophe Tournigand, Raffaele Brustia, Jean Michel Pawlotsky, Jakob Nikolas Kather, Maria Chiara Maiuri, Nicolas Loménie, and Julien Calderaro. Artificial intelligence predicts immune and inflammatory gene signatures directly from hepatocellular carcinoma histology. J. Hepatol., (77)1:116--127, Elsevier BV, July 2022. [PUMA: topic_lifescience artificial deep gene image immune intelligence; learning; pathology; signatures; slide whole]

Katja Hauser, Alexander Kurz, Sarah Haggenmüller, Roman C Maron, Christof von Kalle, Jochen S Utikal, Friedegund Meier, Sarah Hobelsberger, Frank F Gellrich, Mildred Sergon, Axel Hauschild, Lars E French, Lucie Heinzerling, Justin G Schlager, Kamran Ghoreschi, Max Schlaak, Franz J Hilke, Gabriela Poch, Heinz Kutzner, Carola Berking, Markus V Heppt, Michael Erdmann, Sebastian Haferkamp, Dirk Schadendorf, Wiebke Sondermann, Matthias Goebeler, Bastian Schilling, Jakob N Kather, Stefan Fröhling, Daniel B Lipka, Achim Hekler, Eva Krieghoff-Henning, and Titus J Brinker. Explainable artificial intelligence in skin cancer recognition: A systematic review. Eur. J. Cancer, (167):54--69, Elsevier BV, May 2022. [PUMA: topic_lifescience Artificial Dermatology; Man-machine Skin Systematic intelligence; neoplasms; review systems;]

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, and Jakob Nikolas Kather. Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology. Med. Image Anal., (79)102474:102474, Elsevier BV, July 2022. [PUMA: topic_lifescience Artificial Computational Convolutional Learning; Multiple-Instance Vision Weakly-supervised deep intelligence; learning networks; neural pathology; transformers;]

Peter Leonard Schrammen, Narmin Ghaffari Laleh, Amelie Echle, Daniel Truhn, Volkmar Schulz, Titus J Brinker, Hermann Brenner, Jenny Chang-Claude, Elizabeth Alwers, Alexander Brobeil, Matthias Kloor, Lara R Heij, Dirk Jäger, Christian Trautwein, Heike I Grabsch, Philip Quirke, Nicholas P West, Michael Hoffmeister, and Jakob Nikolas Kather. Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology. J. Pathol., (256)1:50--60, Wiley, January 2022. [PUMA: topic_lifescience Lynch artificial cancer; colorectal computational deep digital instability intelligence; learning; microsatellite pathology; syndrome;]

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: topic_lifescience AI; LNM adipose and artificial biomarker; bowel cancer; colorectal deep digital early inflamed intelligence; learning; metastasis; new pT1 pT2 pathology; prediction predictive tissue;]

Didem Cifci, Sebastian Foersch, and Jakob Nikolas Kather. Artificial intelligence to identify genetic alterations in conventional histopathology. J. Pathol., (257)4:430--444, Wiley, July 2022. [PUMA: topic_lifescience analysis; artificial biomarker; image intelligence; oncology precision]

Roman C Maron, Achim Hekler, Sarah Haggenmüller, Christof von Kalle, Jochen S Utikal, Verena Müller, Maria Gaiser, Friedegund Meier, Sarah Hobelsberger, Frank F Gellrich, Mildred Sergon, Axel Hauschild, Lars E French, Lucie Heinzerling, Justin G Schlager, Kamran Ghoreschi, Max Schlaak, Franz J Hilke, Gabriela Poch, Sören Korsing, Carola Berking, Markus V Heppt, Michael Erdmann, Sebastian Haferkamp, Dirk Schadendorf, Wiebke Sondermann, Matthias Goebeler, Bastian Schilling, Jakob N Kather, Stefan Fröhling, Daniel B Lipka, Eva Krieghoff-Henning, and Titus J Brinker. Model soups improve performance of dermoscopic skin cancer classifiers. Eur. J. Cancer, (173):307--316, Elsevier BV, September 2022. [PUMA: topic_lifescience Artificial Calibration; Deep Dermatology; Ensembles; Generalisation; Melanoma; Model Nevus; Robustness intelligence; learning; soups;]

Chiara Maria Lavinia Loeffler, Nadina Ortiz Bruechle, Max Jung, Lancelot Seillier, Michael Rose, Narmin Ghaffari Laleh, Ruth Knuechel, Titus J Brinker, Christian Trautwein, Nadine T Gaisa, and Jakob N Kather. Artificial intelligence-based detection of FGFR3 mutational status directly from routine histology in bladder cancer: A possible preselection for molecular testing?. Eur. Urol. Focus, (8)2:472--479, Elsevier BV, March 2022. [PUMA: topic_lifescience Artificial Bladder Deep FGFR3 Molecular cancer; factor fibroblast for growth intelligence; learning; mutations; receptor testing therapy]