Lukas Fischer, Ph.D.
M. Ambros, M. Berneder, L. Fischer, M. Feuerstein, A. Hanbury, I. Ambros, P. Ambros, S. Taschner-Mandl. Investigation of genetic tumor heterogeneity using correlated images of H&E and fish stained consecutive tissue sections. November, 2019.
H. Eghbal-zadeh, L. Fischer, T. Hoch. On conditioning GANs to hierarchical ontologies. In G. Anderst-Kotsis, A. Tjoa, I. Khalil, E. al. (editors), Database and Expert Systems Applications - Proc DEXA 209 International Workshops, Communications in Computer and Information Science, volume 1062, pages 182-186, DOI 10.1007/978-3-030-27684-3_23, Springer, August, 2019.
M. Ambros, M. Berneder, L. Fischer, M. Feuerstein, A. Hanbury, I. Ambros, P. Ambros, S. Taschner-Mandl. Spot detectoion at the single cell level: A computational approach to analyse ifish spots in consecutive sections of heterogeneous neuroblastoma tumors. pages Poster, June, 2019.
D. Lazic, F. Kromp, F. Rifatbegovic, L. Fischer, C. Ostalecki, I. Ambros, P. Ambros, S. Taschner-Mandl. Characterizing new tumor biomarkers and the microenvironment of the metastatic bone marrow niche in stage M neuroblastoma using quantitative imaging and deep-learning based feature extraction. pages Poster, October, 2018.
F. Kromp, S. Taschner-Mandl, M. Ambros, M. Berneder, L. Fischer, M. Feuerstein, A. Hanbury, I. Ambros, P. Ambros. Deep learning-based tool to analyze I-FISH spots in consecutive sections of heterogeneously amplified neuroblastoma tumors. May, 2018.
H. Eghbal-zadeh, L. Fischer, N. Popitsch, F. Kromp, S. Taschner-Mandl, K. Koutini, T. Gerber, E. Bozsaky, P. Ambros, I. Ambros, G. Widmer, B. Moser. Deep SNP: An End-to-end Deep Neural Network with Attention-based Localization for Break-point Detection in SNP Array Genomic data. pages Poster, July, 2018.
H. Eghbal-zadeh, L. Fischer, N. Popitsch, F. Kromp, S. Taschner-Mandl, T. Gerber, E. Bozsaky, P. Ambros, I. Ambros, G. Widmer, B. Moser. Deep SNP: An end-to-end deep neural network with attention-based localization for break-point detection in SNP array genomic data. Journal of Computational Biology, DOI: 10.1089/cmb.2018.0172, December, 2018.
N. Shepeleva, T. Hoch, L. Fischer, W. Kloihofer, B. Moser. Removing nuisance in tracklet data. In H. Bouma, R. Prabhu, R. Stokes, Y. Yitzjaky (editors), Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies II - Proc. SPIE 2018, volume 10802, pages 08020S, DOI 10.1117/12.2325636, SPIE, November, 2018.
D. Lazic, F. Kromp, F. Rifatbegovic, L. Fischer, C. Ostalecki, I. Ambros, P. Ambros, S. Taschner-Mandl. Towards characterizing new biomarkers for disseminated tumor cells and the microenvironment of the metastatic bone marrow niche in stage M neuroblastoma. pages Poster, October, 2018.
E. Bozsaky, T. Gerber, F. Rifatbegovic, R. Abbasi, N. Popitsch, M. Kauer, L. Fischer, H. Eghbal-zadeh, K. Bühler, S. Alemzadeh, K. Matkovic, A. Kriegner, J. Palme, M. Dulovits, A. Hanbury, M. Lupu, A. Aziz Taha, A. Bampoulidis, C. Ostalecki, M. Kampel, M. Atanasov, M. Berneder, A. Ziegler, I. Ambros, S. Taschner-Mandl, P. Ambros. VISIOMICS - Platform supporting an integrated analysis of image and multiOMICs data for biology based advanced tumor diagnostics. October, 2018.
S. Baumbach, J. Binder, A. Synek, F. Mück, Y. Chevalier, E. Euler, G. Langs, L. Fischer. Analysis of the three-dimensional anatomical variance of the distal radius using 3D shape models. BMC Med Imaging, volume 17, number 1, DOI: 10.1186/s12880-017-0193-9, March, 2017.
Lukas Fischer is Research Manager for Data Science at the Software Competence Center Hagenberg.
He did his MSc in Medical Informatics at the TU Wien (TUW) with focus on medical image segmentation, statistical shape models, image registration, bio-inspired optimization algorithms and machine learning. He continued his PhD studies and research in the domain of medical imaging/medical physics as a research assistant at the Computational Imaging Research Lab (CIR) at the Medical University of Vienna (MUW). His research focus was on the computer vision based quantification of trabecular microarchitecture in patients suffering from severe osteoporosis after lung transplantation.
His current main research interests are in Machine Learning, with special focus on Deep Learning, Generative Models (e.g. GANs, VAEs, AEs), Computer Vision, especially on medical data (e.g. segmentation, registration, classification, and tracking), Statistical Shape Models as well as Transfer Learning and Privacy Preserving Learning aspects.
In addition to his research activities, he is an experienced project manager of various national and international research projects, for example TRESSPASS (H2020 SEC-15-BES-2017), VISIOMICS (FFG COIN), and deepTrust (FFG COMET).