Trabecular bone class mapping across resolutions: translating methods from HR-pQCT to clinical CT. - Alexander Valentinitsch; Lukas Fischer 001; Janina Patsch; Jan Bauer; Franz Kainberger; Georg Langs; Matthew D. DiFranco (Image Processing, 2015)

Analysis of the three-dimensional anatomical variance of the distal radius using 3D shape models. - Sebastian F. Baumbach; Jakob Binder; Alexander Synek; Fabian G. Mück; Yan Chevalier; Ekkehard Euler; Georg Langs; Lukas Fischer 001 (BMC Medical Imaging, 2017)

Deep SNP: An End-to-end Deep Neural Network with Attention-based Localization for Break-point Detection in SNP Array Genomic data. - Hamid Eghbal-zadeh; Lukas Fischer 001; Niko Popitsch; Florian Kromp; Sabine Taschner-Mandl; Khaled Koutini; Teresa Gerber; Eva Bozsaky; Peter F. Ambros; Inge M. Ambros; Gerhard Widmer; Bernhard Alois Moser (CoRR, 2018)

On Conditioning GANs to Hierarchical Ontologies. - Hamid Eghbal-zadeh; Lukas Fischer 001; Thomas Hoch (DEXA Workshops, 2019)

Deep Learning architectures for generalized immunofluorescence based nuclear image segmentation. - Florian Kromp; Lukas Fischer 001; Eva Bozsaky; Inge Ambros; Wolfgang Doerr; Sabine Taschner-Mandl; Peter Ambros; Allan Hanbury (CoRR, 2019)

DeepSNP: An End-to-End Deep Neural Network with Attention-Based Localization for Breakpoint Detection in Single-Nucleotide Polymorphism Array Genomic Data. - Hamid Eghbal-Zadeh; Lukas Fischer 001; Niko Popitsch; Florian Kromp; Sabine Taschner-Mandl; Teresa Gerber; Eva Bozsaky; Peter F. Ambros; Inge M. Ambros; Gerhard Widmer; Bernhard Alois Moser (J. Comput. Biol., 2019)

Applying AI in Practice: Key Challenges and Lessons Learned. - Lukas Fischer 001; Lisa Ehrlinger; Verena Geist; Rudolf Ramler; Florian Sobieczky; Werner Zellinger; Bernhard Moser 001 (CD-MAKE, 2020)

Beyond federated learning: On confidentiality-critical machine learning applications in industry. - Werner Zellinger; Volkmar Wieser; Mohit Kumar 001; David Brunner; Natalia Shepeleva; Rafa Gálvez; Josef Langer; Lukas Fischer 001; Bernhard Moser 001 (ISM, 2020)

Membership-Mappings for Data Representation Learning: Measure Theoretic Conceptualization. - Mohit Kumar 001; Bernhard Moser 001; Lukas Fischer 001; Bernhard Freudenthaler (DEXA Workshops, 2021)

Membership-Mappings for Data Representation Learning: A Bregman Divergence Based Conditionally Deep Autoencoder. - Mohit Kumar 001; Bernhard Moser 001; Lukas Fischer 001; Bernhard Freudenthaler (DEXA Workshops, 2021)

Membership-Mappings for Data Representation Learning. - Mohit Kumar 001; Bernhard Alois Moser; Lukas Fischer 001; Bernhard Freudenthaler (CoRR, 2021)

Information Theoretic Evaluation of Privacy-Leakage, Interpretability, and Transferability for a Novel Trustworthy AI Framework. - Mohit Kumar 001; Bernhard Alois Moser; Lukas Fischer 001; Bernhard Freudenthaler (CoRR, 2021)

AI System Engineering - Key Challenges and Lessons Learned. - Lukas Fischer 001; Lisa Ehrlinger; Verena Geist; Rudolf Ramler; Florian Sobieczky; Werner Zellinger; David Brunner; Mohit Kumar 001; Bernhard Moser 001 (Mach. Learn. Knowl. Extr., 2021)

Evaluation of Deep Learning Architectures for Complex Immunofluorescence Nuclear Image Segmentation. - Florian Kromp; Lukas Fischer 001; Eva Bozsaky; Inge M. Ambros; Wolfgang Dörr; Klaus Beiske; Peter F. Ambros; Allan Hanbury; Sabine Taschner-Mandl (IEEE Trans. Medical Imaging, 2021)

Towards Practical Secure Privacy-Preserving Machine (Deep) Learning with Distributed Data. - Mohit Kumar 001; Bernhard Moser 001; Lukas Fischer 001; Bernhard Freudenthaler (DEXA Workshops, 2022)

Twenty Years of Successful Translational Research: A Case Study of Three COMET Centers. - Katja Bühler; Cornelia Travnicek; Veronika Nowak; Edgar R. Weippl; Lukas Fischer 001; Rudolf Ramler; Robert Wille (DEXA Workshops, 2022)

2D nnUNet for classification and segmentation of anatomical structures in fetal torso ultrasound. - Clara Dacho; David Gabauer; David Brunner; Lukas Fischer 001 (Image Processing, 2022)

Prescriptive Analytics: When Data- and Simulation-based Models Interact in a Cooperative Way. - Michael Affenzeller; Michael Bögl; Lukas Fischer 001; Florian Sobieczky; Kaifeng Yang; Jan Zenisek (SYNASC, 2022)

How Do Deep-Learning Framework Versions Affect the Reproducibility of Neural Network Models? - Mostafa Shahriari; Rudolf Ramler; Lukas Fischer 001 (Mach. Learn. Knowl. Extr., 2022)

Secure Federated Learning with Kernel Affine Hull Machines. - Mohit Kumar 001; Bernhard Moser 001; Lukas Fischer 001 (ESANN, 2023)

An Information Theoretic Approach to Privacy-Preserving Interpretable and Transferable Learning. - Mohit Kumar 001; Bernhard Alois Moser; Lukas Fischer 001; Bernhard Freudenthaler (Algorithms, 2023)

Kernel Affine Hull Machines for Differentially Private Learning. - Mohit Kumar 001; Bernhard Alois Moser; Lukas Fischer 001 (CoRR, 2023)

Examples of Long-Term Science-Industry Partnerships for Translational Computer Science. - Cornelia Travnicek; Veronika Nowak; Rudolf Ramler; Lukas Fischer 001; Katja Bühler (Comput. Sci. Eng., 2023)

Membership Mappings for Practical Secure Distributed Deep Learning. - Mohit Kumar 001; Weiping Zhang 001; Lukas Fischer 001; Bernhard Freudenthaler (IEEE Trans. Fuzzy Syst., 2023)

Risk Assessment in AI System Engineering: Experiences and Lessons Learned from a Practitioner's Perspective. - Magdalena Fuchs; Lukas Fischer 001; Alessio Montuoro; Mohit Kumar 001; Bernhard Alois Moser (DEXA Workshops, 2024)

On Mitigating the Utility-Loss in Differentially Private Learning: A New Perspective by a Geometrically Inspired Kernel Approach (Abstract Reprint). - Mohit Kumar 001; Bernhard Alois Moser; Lukas Fischer 001 (IJCAI, 2024)

Industrial Experience Report on AI-Assisted Coding in Professional Software Development. - Rudolf Ramler; Michael Moser; Lukas Fischer 001; Markus Nissl; René Heinzl (LLM4CODE@ICSE, 2024)

Responsible AI Engineering: The Case of an Inclusive Image Annotation Team in a Global Technology Company. - Bernhard Schenkenfelder; Ulrich Brandstätter; Lukas Fischer 001; Rudolf Ramler; Dominik Laister; Martin Hartl; Markus Wurm (RAIE@ICSE, 2024)

On Mitigating the Utility-Loss in Differentially Private Learning: A New Perspective by a Geometrically Inspired Kernel Approach. - Mohit Kumar 001; Bernhard Alois Moser; Lukas Fischer 001 (J. Artif. Intell. Res., 2024)