2024W

• Bachelor Thesis / 183.582 / PR
• Interdisciplinary Project in Data Science / 194.147 / PR
• Introduction to Programming 1 / 185.A91 / VU
• Machine Learning for Visual Computing / 183.605 / VU

• MyeFLOW- Automated minimal residual disease assessment in childhood acute myeloid leukemia
  2020 – 2025 / Vienna Business Agency (WAW)
  Publications: 139741 / 139863 / 142178 / 153472 / 190264 / 203811

2024

• FATE: Feature-Agnostic Transformer-based Encoder for learning generalized embedding spaces in flow cytometry data / Weijler, L., Kowarsch, F., Reiter, M., Hermosilla, P., Maurer-Granofszky, M., & Dworzak, M. (2024). FATE: Feature-Agnostic Transformer-based Encoder for learning generalized embedding spaces in flow cytometry data. In 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (pp. 7941–7949). https://doi.org/10.1109/WACV57701.2024.00777
  Project: MyeFLOW (2020–2025)

2023

• Explainable Visualization Techniques for Transformers in Flow Cytometry Data / Kowarsch, F., Weijler, L. M., Wödlinger, M. G., Kleber, F., Maurer-Granofszky, M., Reiter, M., & Dworzak, M. (2023, February 15). Explainable Visualization Techniques for Transformers in Flow Cytometry Data [Conference Presentation]. 26th Computer Vision Winter Workshop (CVWW) 2023, Krems an der Donau, Austria.
• FCM marker importance for MRD assessment in T-cell acute lymphoblastic leukemia: An AIEOP-BFM-ALL-FLOW study group report / Kowarsch, F., Maurer-Granofszky, M., Weijler, L., Wödlinger, M., Reiter, M., Schumich, A., Feuerstein, T., Sala, S., Nováková, M., Faggin, G., Gaipa, G., Hrusak, O., Buldini, B., & Dworzak, M. (2023). FCM marker importance for MRD assessment in T-cell acute lymphoblastic leukemia: An AIEOP-BFM-ALL-FLOW study group report. Cytometry Part A. https://doi.org/10.1002/cyto.a.24805
  Project: MyeFLOW (2020–2025)

2022

• Automated identification of cell populations in flow cytometry data with transformers / Wödlinger, M., Reiter, M., Weijler, L., Maurer-Granofszky, M., Schumich, A., Sajaroff, E., Groeneveld-Krentz, S., Rossi Jorge, Karawajew, L., Ratei, R., & Dworzak, M. (2022). Automated identification of cell populations in flow cytometry data with transformers. Computers in Biology and Medicine, 144, Article 105314. https://doi.org/10.1016/j.compbiomed.2022.105314
  Project: MyeFLOW (2020–2025)
• UMAP Based Anomaly Detection for Minimal Residual Disease Quantification within Acute Myeloid Leukemia / Weijler, L., Kowarsch, F., Wödlinger, M., Reiter, M., Maurer-Granofszky, M., Schumich, A., & Dworzak, M. N. (2022). UMAP Based Anomaly Detection for Minimal Residual Disease Quantification within Acute Myeloid Leukemia. Cancers, 14(4), Article 898. https://doi.org/10.3390/cancers14040898
  Project: MyeFLOW (2020–2025)
• Towards Self-explainable Transformers for Cell Classification in Flow Cytometry Data / Kowarsch, F., Weijler, L., Wödlinger, M., Reiter, M., Maurer-Granofszky, M., Schumich, A., Sajaroff, E., Groeneveld-Krentz, S., Rossi, J., Karawajew, L., Ratei, R., & Dworzak, M. (2022). Towards Self-explainable Transformers for Cell Classification in Flow Cytometry Data. In Interpretability of Machine Intelligence in Medical Image Computing (pp. 22–32). https://doi.org/10.1007/978-3-031-17976-1_3
  Project: MyeFLOW (2020–2025)

2021

• Detecting Rare Cell Populations in Flow Cytometry Data Using UMAP / Weijler, L., Diem, M., Reiter, M., & Maurer-Granofszky, M. (2021). Detecting Rare Cell Populations in Flow Cytometry Data Using UMAP. In 2020 25th International Conference on Pattern Recognition (ICPR). The 25th International Conference on Pattern Recognition (ICPR 2020), Mailand, Italy. https://doi.org/10.1109/icpr48806.2021.9413180

2019

• Automated Flow Cytometric MRD Assessment in Childhood Acute B‐ Lymphoblastic Leukemia Using Supervised Machine Learning / Reiter, M., Diem, M., Schumich, A., Maurer-Granofszky, M., Karawajew, L., Rossi, J. G., Ratei, R., Groenefeld-Krentz, S., Sajaroff, E., Suhendra, S., Kampel, M., & Dworzak, M. (2019). Automated Flow Cytometric MRD Assessment in Childhood Acute B‐ Lymphoblastic Leukemia Using Supervised Machine Learning. Cytometry Part A, 95(9), 966–975. https://doi.org/10.1002/cyto.a.23852
• Aneuploidy in children with relapsed B-cell precursor acute lymphoblastic leukaemia: clinical importance of detecting a hypodiploid origin of relapse / Groeneveld-Krentz, S., Schröder, M., Reiter, M., Pogodzinski, M., Pimentel-Gutiérrez, H., Vagkopoulou, R., Hof, J., Chen-Santel, C., Nebral, K., Bradtke, J., Türkmen, S., Baldus, C., Gattenlöhner, S., Haas, O., von Stackelberg, A., Karawajew, L., Eckert, C., & Kirschner-Schwabe, R. (2019). Aneuploidy in children with relapsed B-cell precursor acute lymphoblastic leukaemia: clinical importance of detecting a hypodiploid origin of relapse. British Journal of Haematology, 185(2), 266–283. https://doi.org/10.1111/bjh.15770
• Quantifying Minimal Residual Disease in Flow Cytometry Measurements of Acute Lymphoblastic Leukemia Patients Automatically / Diem, M., Reiter, M., Kleber, F., & Dworzak, M. (2019). Quantifying Minimal Residual Disease in Flow Cytometry Measurements of Acute Lymphoblastic Leukemia Patients Automatically. Deutsche Gesellschaft für Zytometrie - Annual Meeting, Berlin, Germany. http://hdl.handle.net/20.500.12708/86943
• GMM Interpolation for Blood Cell Cluster Alignment in Childhood Leukaemia / Licandro, R., Miloserdov, K., Reiter, M., & Kampel, M. (2019). GMM Interpolation for Blood Cell Cluster Alignment in Childhood Leukaemia. In A. Pichler, P. M. Roth, R. Sablatnig, G. Stübl, & M. Vincze (Eds.), Proceedings of the ARW & OAGM Workshop 2019 (pp. 179–182). Verlag der Technischen Universität Graz. https://doi.org/10.3217/978-3-85125-663-5-39
• FlowMe - A Clinical Decision Support System for Minimal Residual Disease Quantification for Acute Lymphoblastic Leukemia / Diem, M., Kleber, F., Reiter, M., Maurer-Granofzsky, M., Schumich, A., & Dworzak, M. (2019). FlowMe - A Clinical Decision Support System for Minimal Residual Disease Quantification for Acute Lymphoblastic Leukemia. AlpenFlow 2019, Bad Ischl, Austria. http://hdl.handle.net/20.500.12708/86944

2018

• Automation of flow-cytometric therapy response assessment in paediatric acute leukaemia using machine learning / Reiter, M., Diem, M., Maurer-Granofsky, M., Schuhmich, A., & Dworzak, M. (2018). Automation of flow-cytometric therapy response assessment in paediatric acute leukaemia using machine learning. From Lab to Life" Childhood Cancer Research Initiatives Symposium celebrating the 30th anniversary of the CCRI, Wien, Austria. http://hdl.handle.net/20.500.12708/86785
• Monitoring Acute Lymphoblastic Leukaemia Therapy with Stacked Denoising Autoencoders / Scheithe, J., Licandro, R., Rota, P., Reiter, M., Diem, M., & Kampel, M. (2018). Monitoring Acute Lymphoblastic Leukaemia Therapy with Stacked Denoising Autoencoders. In Computer Aided Intervention and Diagnostics in Clinical and Medical Images. International Conference on Clinical and Medical Image Analysis (ICCMIA18), Tamilnadu, Indien, Non-EU. Springer. http://hdl.handle.net/20.500.12708/57569
• Application of Machine Learning for Automatic MRD Assessment in Paediatric Acute Myeloid Leukaemia / Licandro, R., Reiter, M., Diem, M., Dworzak, M., Schumich, A., & Kampel, M. (2018). Application of Machine Learning for Automatic MRD Assessment in Paediatric Acute Myeloid Leukaemia. In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods. 7th International Conference on Pattern Recognition Applications and Methods (ICPRAM), Funchal, Portugal, EU. ScitePress. https://doi.org/10.5220/0006595804010408
• WGAN Latent Space Embeddings for Blast Identification in Childhood Acute Myeloid Leukaemia / Licandro, R., Schlegl, T., Reiter, M., Diem, M., Dworzak, M., Schumich, A., Langs, G., & Kampel, M. (2018). WGAN Latent Space Embeddings for Blast Identification in Childhood Acute Myeloid Leukaemia. In 2018 24th International Conference on Pattern Recognition (ICPR). 24th International Conference on Pattern Recognition (ICPR) 2018, Beijing, China, Non-EU. IEEE. https://doi.org/10.1109/icpr.2018.8546177

2016

• Clustering of cell populations in flow cytometry data using a combination of Gaussian mixtures / Reiter, M., Rota, P., Kleber, F., Diem, M., Groenefeld-Krentz, S., & Dworzak, M. (2016). Clustering of cell populations in flow cytometry data using a combination of Gaussian mixtures. Pattern Recognition, 60, 1029–1040. https://doi.org/10.1016/j.patcog.2016.04.004
• Assessment of Flow Cytometry Data Using a Combination of Gaussian Mixture Models / Reiter, M. (2016). Assessment of Flow Cytometry Data Using a Combination of Gaussian Mixture Models. Workshop on the ICT Contribution to the Development of Clinical Applications, Wien, Austria. http://hdl.handle.net/20.500.12708/86429
• AutoFLOW: a novel heuristic method to automatically detect leukaemic cells in flow cytometric data / Licandro, R., Rota, P., Reiter, M., & Kampel, M. (2016). AutoFLOW: a novel heuristic method to automatically detect leukaemic cells in flow cytometric data. 3rd Austrian Biomarker Symposium 2016 on early diagnostics, Wien, Austria. http://hdl.handle.net/20.500.12708/86368
• Automatic Detection of Leukaemic Cells in Flow Cytometric Data for Minimal Residual Disease Assessment / Licandro, R., Rota, P., Reiter, M., Kleber, F., Diem, M., & Kampel, M. (2016). Automatic Detection of Leukaemic Cells in Flow Cytometric Data for Minimal Residual Disease Assessment. EuroScience Open forum (ESOF) - Marie Sklodowska-Curie Actions Satellite Event `Research and Society’, Manchester, Großbritannien, EU. http://hdl.handle.net/20.500.12708/86365
• The Role of Machine Learning in Medical Data Analysis. A Case Study: Flow Cytometry / Rota, P., Kleber, F., Reiter, M., Groenefeld-Krentz, S., & Kampel, M. (2016). The Role of Machine Learning in Medical Data Analysis. A Case Study: Flow Cytometry. In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP 2016), Rom, Italien, EU. https://doi.org/10.5220/0005675903030310
• Flow Cytometry based automatic MRD assessment in Acute Lymphoblastic Leukaemia: Longitudinal evaluation of time-specific cell population models / Licandro, R., Rota, P., Reiter, M., & Kampel, M. (2016). Flow Cytometry based automatic MRD assessment in Acute Lymphoblastic Leukaemia: Longitudinal evaluation of time-specific cell population models. In 2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI). 14th International Workshop on Content-based Multimedia Indexing, Bukarest, Rumänien, EU. IEEE. https://doi.org/10.1109/cbmi.2016.7500274

2015

• On Automated Flow Cytometric Analysis for MRD Estimation of Acute Lymphoblastic Leukaemia: A Comparison Among Different Approaches / Rota, P., Groenefeld-Krentz, S., & Reiter, M. (2015). On Automated Flow Cytometric Analysis for MRD Estimation of Acute Lymphoblastic Leukaemia: A Comparison Among Different Approaches. In Proceedings of IEEE International Conference on Bioinformatics and Biomedicine. IEEE International Conference on Bioinformatics and Biomedicine, Washington, USA, Non-EU. IEEE. http://hdl.handle.net/20.500.12708/56172

2014

• Semi-Automated Segmentation of Neuroblastoma Nuclei Using the Gradient Energy Tensor: A User Driven Approach / Kromp, F., Taschner-Mandl, S., Schwarz, M., Blaha, J., Ambros, P., & Reiter, M. (2014). Semi-Automated Segmentation of Neuroblastoma Nuclei Using the Gradient Energy Tensor: A User Driven Approach. In 7th International Conference on Machine Vision (ICMV). 7th International Conference on Machine Vision (ICMV), Mailand, Italien, EU. http://hdl.handle.net/20.500.12708/55764
• Towards automation of flow cytometric analysis for quality-assured follow-up assessment to guide curative therapy for acute lympholastic leukaemia in children / Reiter, M., Hoffmann, J., Kleber, F., Schumich, A., Peter, G., Kromp, F., Kampel, M., & Dworzak, M. (2014). Towards automation of flow cytometric analysis for quality-assured follow-up assessment to guide curative therapy for acute lympholastic leukaemia in children. Magazine of European Medical Oncology, 7(4), 219–226. https://doi.org/10.1007/s12254-014-0172-6
• AutoFLOW - Automation of Flow Cytometric Analysis for Quality-Assured Follow-up Assessment to Guide Curative Therapy for Acute Lymphoblastic Leukaemia in Children / Reiter, M., Kleber, F., Kampel, M., & Dworzak, M. (2014). AutoFLOW - Automation of Flow Cytometric Analysis for Quality-Assured Follow-up Assessment to Guide Curative Therapy for Acute Lymphoblastic Leukaemia in Children. 2nd Austrian Biomarker Symposium Vienna, Wien, Austria. http://hdl.handle.net/20.500.12708/85910
• Automation of MRD Measurements in Flow Cytometry to Guide Curative Therapy for ALL in Children / Reiter, M., Kleber, F., Hoffmann, J., & Dworzak, M. (2014). Automation of MRD Measurements in Flow Cytometry to Guide Curative Therapy for ALL in Children. In 4th Munich Biomarker Conference Abstracts (p. 53). http://hdl.handle.net/20.500.12708/55290

2010

• Visual Optimality and Stability Analysis of 3DCT Scan Positions / Amirkhanov, A., Heinzl, C., Reiter, M., & Gröller, E. (2010). Visual Optimality and Stability Analysis of 3DCT Scan Positions. IEEE Transactions on Visualization and Computer Graphics, 16(6), 1477–1486. https://doi.org/10.1109/tvcg.2010.214

2006

• Active Feature Models / Langs, G., Peloschek, P. L., Donner, R., Reiter, M., & Bischof, H. (2006). Active Feature Models. In The 18th International Conference on Pattern Recognition (pp. 417–420). IEEE Society. http://hdl.handle.net/20.500.12708/51558
• Predicting Near Infrared Face Texture from Color Face Images using Canonical Correlation Analysis / Reiter, M., Donner, R., Langs, G., & Bischof, H. (2006). Predicting Near Infrared Face Texture from Color Face Images using Canonical Correlation Analysis. In Digital Imaging and Pattern Recognition (pp. 161–167). OCG Schriftenreihe. http://hdl.handle.net/20.500.12708/51584
• Estimation of Face Depth Maps from Color Textures using Canonical Correlation Analysis / Reiter, M., Donner, R., Langs, G., & Bischof, H. (2006). Estimation of Face Depth Maps from Color Textures using Canonical Correlation Analysis. In Proceedings of the Computer Vision Winter Workshop (pp. 17–21). http://hdl.handle.net/20.500.12708/51585
• 3D and Infrared Face Reconstruction from RGB data using Canonical Correlation Analysis / Reiter, M., Donner, R., Langs, G., & Bischof, H. (2006). 3D and Infrared Face Reconstruction from RGB data using Canonical Correlation Analysis. In The 18th International Conference on Pattern Recognition. IAPR ICPR 2006, Hong Kong, Non-EU. IEEE Conmputer Society. http://hdl.handle.net/20.500.12708/51586
• Fast Active Appearance Model Search Using Canonical Correlation Analysis / Donner, R., Reiter, M., Langs, G., Peloschek, P. L., & Bischof, H. (2006). Fast Active Appearance Model Search Using Canonical Correlation Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(10), 1690–1694. http://hdl.handle.net/20.500.12708/173411

2005

• CCA-based Active Appearance Model Search / Donner, R., Langs, G., Reiter, M., & Bischof, H. (2005). CCA-based Active Appearance Model Search. In A. Hanbury & H. Bischof (Eds.), Computer Vision Winter Workshop 2005 (pp. 73–82). Eigenverlag. http://hdl.handle.net/20.500.12708/51329

• U-Net based classification of flow cytometry data / Toth, T. (2023). U-Net based classification of flow cytometry data [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.101968
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• Self-explaining transformers for cell population detection in flow cytometry data / Kowarsch, F. (2023). Self-explaining transformers for cell population detection in flow cytometry data [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.102742
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• Robo-Smile: Development of a facial expression feedback system for an emotion learning platform for children with ASD / Weinert, N. M. (2021). Robo-Smile: Development of a facial expression feedback system for an emotion learning platform for children with ASD [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2021.83098
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• Detection of rare cell populations in flow cytometry data with small training sets / Weijler, L. M. (2020). Detection of rare cell populations in flow cytometry data with small training sets [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2020.67421
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• Semi-automated quantification of tumor marker expression based on robust image segmentation and classification methods / Kromp, F. (2014). Semi-automated quantification of tumor marker expression based on robust image segmentation and classification methods [Diploma Thesis, Technische Universität Wien]. reposiTUm. http://hdl.handle.net/20.500.12708/79449