Lisa Magdalena Weijler
Univ.Ass.in Dipl.-Ing.in / BSc
Role
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PreDoc Researcher
Computer Vision, E193-01
Courses
2023W
- Fundamentals of Computer Vision / 193.125 / VU
- Machine Learning for Visual Computing / 183.605 / VU
- Project Medical Computer Science / 193.080 / PR
2024S
- Deep Learning for Visual Computing / 183.663 / VU
Publications
- 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.
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FCM marker importance for MRD assessment in T-cell acute lymphoblastic leukemia: An AIEOP-BFM-ALL-FLOW study group report
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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) -
UMAP based Anomaly Detection for Leukemic Cell Quantification within Acute Myeloid Leukemia
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Weijler, L. M. (2022, February 20). UMAP based Anomaly Detection for Leukemic Cell Quantification within Acute Myeloid Leukemia [Conference Presentation]. ICMVA 2022: 2022 the 5th International Conference on Machine Vision and Applications (ICMVA), Singapur, Singapore.
Project: MyeFLOW (2020–2025) -
Automated identification of cell populations in flow cytometry data with transformers
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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
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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
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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) - 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
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Detection of rare cell populations in flow cytometry data with small training sets
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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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