2024W

• Fundamentals of Computer Vision / 193.125 / VU
• Machine Learning for Visual Computing / 183.605 / VU

• Efficient continuous group convolutions for local SO(3) equivariance in 3D point clouds / Weijler, L. M., & Hermosilla, P. (2024, February 15). Efficient continuous group convolutions for local SO(3) equivariance in 3D point clouds [Conference Presentation]. COMPUTER VISION WINTER WORKSHOP 2024, Slovenia.
• 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)
• 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)
• UMAP based Anomaly Detection for Leukemic Cell Quantification within Acute Myeloid Leukemia / 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 / 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)
• 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
• 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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