2024W

• Applications of Computer Vision / 183.590 / EX

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

• ECSIC: Epipolar Cross Attention for Stereo Image Compression / Wodlinger, M., Kotera, J., Keglevic, M., Xu, J., & Sablatnig, R. (2024). ECSIC: Epipolar Cross Attention for Stereo Image Compression. In 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (pp. 3424–3433). https://doi.org/10.1109/WACV57701.2024.00340
  Project: AISTREAM (2021–2022)
• Impact of Learned Domain Specific Compression on Satellite Image Object Classification / Bayerl, A., Keglevic, M., Wödlinger, M. G., & Sablatnig, R. (2023). Impact of Learned Domain Specific Compression on Satellite Image Object Classification. In Proceedings of the 26th Computer Vision Winter Workshop (CVWW 2023). 26th Computer Vision Winter Workshop (CVWW) 2023, Krems an der Donau, Austria. CEUR-WS.org. https://doi.org/10.34726/5331
  Download: Paper (2.36 MB)
  Project: AISTREAM (2021–2022)
• 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)
• Learned Lossy Image Compression for Volumetric Medical Data / Kotera, J., Wödlinger, M. G., & Keglevic, M. (2023). Learned Lossy Image Compression for Volumetric Medical Data. In R. Sablatnig & F. Kleber (Eds.), Proceedings of the 26th Computer Vision Winter Workshop (CVWW 2023). CEUR-WS.org. https://doi.org/10.34726/5302
  Download: PDF (1.39 MB)
  Project: AISTREAM (2021–2022)
• SASIC: Stereo Image Compression With Latent Shifts and Stereo Attention / Wödlinger, M. G., Kotera, J., Xu, J., & Sablatnig, R. (2022). SASIC: Stereo Image Compression With Latent Shifts and Stereo Attention. In Proceedings. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 651–660). Institute of Electrical and Electronic Engineers, Inc (IEEE). https://doi.org/10.34726/3564
  Download: PDF (3.26 MB)
  Project: AISTREAM (2021–2022)
• 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)
• Artist-specific style transfer for semantic segmentation of paintings: The value of large corpora of surrogate artworks / Heitzinger, T., Woedlinger, M., & Stork, D. G. (2022). Artist-specific style transfer for semantic segmentation of paintings: The value of large corpora of surrogate artworks. In Proc. IS&T Int’l. Symp. on Electronic Imaging: Computer Vision and Image Analysis of Art (pp. 186-1-186–6). https://doi.org/10.2352/EI.2022.34.13.CVAA-186
• 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)
• Text Baseline Recognition Using a Recurrent Convolutional Neural Network / Wödlinger, M., & Sablatnig, R. (2021). Text Baseline Recognition Using a Recurrent Convolutional Neural Network. 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/icpr48806.2021.9412624
• Classification and Segmentation of Scanned Library Catalogue Cards using Convolutional Neural Networks / Wödlinger, M., & Sablatnig, R. (2020). Classification and Segmentation of Scanned Library Catalogue Cards using Convolutional Neural Networks. In P. M. Roth, G. Steinbauer, F. Fraundorfer, M. Brandstötter, & R. Perko (Eds.), Proceedings of the Joint Austrian Computer Vision and Robotics Workshop 2020 (pp. 90–91). Austrian Association for Pattern Recognition (ÖAGM/AAPR). https://doi.org/10.3217/978-3-85125-752-6