Dr. Florian Kromp
Research Team Lead Computer Vision and Representation Learning
Details
Florian Kromp is a Senior Researcher in Data Science and Research Team Lead Computer Vision and Representation Learning.
Research Interests
- Visual anomaly detection
- AI for Assisted reproductive medicine
- Multi-modal representation learning
Key Facts
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Honours and Awards
- Pattern Recognition and Image Processing (PRIP, TU Wien) Award 2009
Publikationen
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2025
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Human Perception for Print Defect Detection
Brunner, L., Pönitz, T., & Kromp, F.. (2026). Human perception for print defect detection. In Eighteenth International Conference on Machine Vision (ICMV 2025) (p. 81). SPIE. https://doi.org/10.1117/12.3096940
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Collaborative Trustworthy Foundation Model Framework: An Environmental Sustainability Use-Case to Detect Contamination Objects in Organic Waste Streams
Valentinitsch, A., Bencik, B., Brucker, M., Lammer, G., Adami, C., Kumar, M., Fischer, L., & Kromp, F.. (2025). Collaborative Trustworthy Foundation Model Framework: An Environmental Sustainability Use-Case to Detect Contamination Objects in Organic Waste Streams. In Communications in Computer and Information Science (pp. 30–44). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-02003-1_3
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2024
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Robustness of One-Class Visual Anomaly Detection Methods
Kromp, F., Brunner, D., Zellinger, J., Valentinitsch, A., Cakiroglu, O., Schachner, C., & Fischer, L.. (2025). Robustness of One-Class Visual Anomaly Detection Methods. In Bernhard Nessler (p. and Vision (AIRoV)). innsbruck university press. https://doi.org/10.15203/99106-150-2-35
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2023
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An annotated human blastocyst dataset to benchmark deep learning architectures for in vitro fertilization
Kromp, F., Wagner, R., Balaban, B., Cottin, V., Cuevas-Saiz, I., Schachner, C., Fancsovits, P., Fawzy, M., Fischer, L., Findikli, N., Kovačič, B., Ljiljak, D., Martínez-Rodero, I., Parmegiani, L., Shebl, O., Min, X., & Ebner, T. (2023). An annotated human blastocyst dataset to benchmark deep learning architectures for in vitro fertilization. Scientific Data, 10(1).
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A human blastocyst dataset including clinical annotations to benchmark deep learning architectures for in vitro fertilization
Kromp, F., Wagner, R., Balaban, B., Cottin, V., Saiz, I. C., Dacho, C., Fancsovits, P., Fawzy, M., Fischer, L., Findikli, N., Kovacic, B., Ljiljak, D., Rodero, I. M., Parmegiani, L., Shebl, O., Min, X., & Ebner, T.. (2023). A human blastocyst dataset including clinical annotations to benchmark deep learning architectures for in vitro fertilization. In None (p. None). figshare. https://doi.org/10.6084/M9.FIGSHARE.20123153
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2022
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Potential of a Deep Convolutional Neural Network in the Selection of Human Blastocysts to Predict Clinical Pregnancy and Live Birth
Ebner, T., Wagner, R., Fischer, L., Shebl, O., Dacho, C., & Kromp, F. (2022). POTENTIAL OF A DEEP CONVOLUTIONAL NEURAL NETWORK IN THE SELECTION OF HUMAN BLASTOCYSTS TO PREDICT CLINCIAL PREGNANCY AND LIVE BIRTH. Fertility and Sterility, 118(4), e132.
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Collaborative aspects of solving rail-track multi-sensor data fusion
Kromp, F., Hinterberger, F., Konanur, D., & Wieser, V. (2022). Collaborative Aspects of Solving Rail-Track Multi-sensor Data Fusion. Database and Expert Systems Applications - DEXA 2022 Workshops, 69–78.
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Detection of the 3D Ground Plane from 2D Images for Distance Measurement to the Ground
Cakiroglu, O., Wieser, V., Zellinger, W., Souza Ribeiro, A., Kloihofer, W., & Kromp, F. (2022). Detection of the 3D Ground Plane from 2D Images for Distance Measurement to the Ground. Database and Expert Systems Applications - DEXA 2022 Workshops, 44–54.
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Artificial intelligence algorithms reach expert-level accuracy in automated grading of blastocyst morphology assessment based on static embryo images and Gardner criteria
Kromp, F., Balaban, B., Cottin, V., Saiz, I. C., Fancsovits, P., Fawzy, M., Findikli, N., Kovacic, B., Ljiljak, D., Rodero, I. M., Parmegiani, L., Shebl, O., Wagner, R., Xie, M., & Ebner, T. (2022). O-285 Artificial intelligence algorithms reach expert-level accuracy in automated grading of blastocyst morphology assessment based on static embryo images and Gardner criteria. Human Reproduction, 37(Supplement_1).
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2021
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Landscape of Bone Marrow Metastasis in Human Neuroblastoma Unraveled by Transcriptomics and Deep Multiplex Imaging
Lazic, D., Kromp, F., Rifatbegovic, F., Repiscak, P., Kirr, M., Mivalt, F., Halbritter, F., Bernkopf, M., Bileck, A., Ussowicz, M., Ambros, I. M., Ambros, P. F., Gerner, C., Ladenstein, R., Ostalecki, C., & Taschner-Mandl, S. (2021). Landscape of Bone Marrow Metastasis in Human Neuroblastoma Unraveled by Transcriptomics and Deep Multiplex Imaging. Cancers, 13(17), 4311.
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Evaluation of Deep Learning architectures for complex immunofluorescence nuclear image segmentation
Kromp, F., Fischer, L., Bozsaky, E., Ambros, I. M., Dorr, W., Beiske, K., Ambros, P. F., Hanbury, A., & Taschner-Mandl, S. (2021). Evaluation of Deep Learning Architectures for Complex Immunofluorescence Nuclear Image Segmentation. IEEE Transactions on Medical Imaging, 40(7), 1934–1949.
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2020
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An annotated fluorescence image dataset for training nuclear segmentation methods
Kromp, F., Bozsaky, E., Rifatbegovic, F., Fischer, L., Ambros, M., Berneder, M., Weiss, T., Lazic, D., Dörr, W., Hanbury, A., Beiske, K., Ambros, P. F., Ambros, I. M., & Taschner-Mandl, S. (2020). An annotated fluorescence image dataset for training nuclear segmentation methods. Scientific Data, 7(1).
Zur Publikation