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Sounds Good? Fast and Secure Contact Exchange in Groups

Autor: Putz, Florentin, Haesler, Steffen, Hollick, Matthias

Publikováno v: Proc. ACM Hum.-Comput. Interact. 8, CSCW2, Article 425 (November 2024), 44 pages

Trustworthy digital communication requires the secure exchange of contact information, but current approaches lack usability and scalability for larger groups of users. We evaluate the usability of two secure contact exchange systems: the current sta

Externí odkaz: http://arxiv.org/abs/2411.13694

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PairSonic: Helping Groups Securely Exchange Contact Information

Autor: Putz, Florentin, Haesler, Steffen, Völkl, Thomas, Gehring, Maximilian, Rollshausen, Nils, Hollick, Matthias

Publikováno v: ACM CSCW 2024

Securely exchanging contact information is essential for establishing trustworthy communication channels that facilitate effective online collaboration. However, current methods are neither user-friendly nor scalable for large groups of users. In res

Externí odkaz: http://arxiv.org/abs/2411.13693

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Data-efficient 4D-STEM in SEM: Beyond 2D Materials to Metallic Materials

Autor: Bansal, Ujjval, Sharma, Amit, Putz, Barbara, Kirchlechner, Christoph, Lee, Subin

Four-dimensional scanning transmission electron microscopy (4D-STEM) is a powerful tool that allows for the simultaneous acquisition of spatial and diffraction information, driven by recent advancements in direct electron detector technology. Althoug

Externí odkaz: http://arxiv.org/abs/2410.17206

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Task-Specific Data Preparation for Deep Learning to Reconstruct Structures of Interest from Severely Truncated CBCT Data

Autor: Huang, Yixing, Fan, Fuxin, Gomaa, Ahmed, Maier, Andreas, Fietkau, Rainer, Bert, Christoph, Putz, Florian

Cone-beam computed tomography (CBCT) is widely used in interventional surgeries and radiation oncology. Due to the limited size of flat-panel detectors, anatomical structures might be missing outside the limited field-of-view (FOV), which restricts t

Externí odkaz: http://arxiv.org/abs/2409.08800

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Fine-Tuning a Local LLaMA-3 Large Language Model for Automated Privacy-Preserving Physician Letter Generation in Radiation Oncology

Autor: Hou, Yihao, Bert, Christoph, Gomaa, Ahmed, Lahmer, Godehard, Hoefler, Daniel, Weissmann, Thomas, Voigt, Raphaela, Schubert, Philipp, Schmitter, Charlotte, Depardon, Alina, Semrau, Sabine, Maier, Andreas, Fietkau, Rainer, Huang, Yixing, Putz, Florian

Generating physician letters is a time-consuming task in daily clinical practice. This study investigates local fine-tuning of large language models (LLMs), specifically LLaMA models, for physician letter generation in a privacy-preserving manner wit

Externí odkaz: http://arxiv.org/abs/2408.10715

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Comprehensive Multimodal Deep Learning Survival Prediction Enabled by a Transformer Architecture: A Multicenter Study in Glioblastoma

Autor: Gomaa, Ahmed, Huang, Yixing, Hagag, Amr, Schmitter, Charlotte, Höfler, Daniel, Weissmann, Thomas, Breininger, Katharina, Schmidt, Manuel, Stritzelberger, Jenny, Delev, Daniel, Coras, Roland, Dörfler, Arnd, Schnell, Oliver, Frey, Benjamin, Gaipl, Udo S., Semrau, Sabine, Bert, Christoph, Fietkau, Rainer, Putz, Florian

Background: This research aims to improve glioblastoma survival prediction by integrating MR images, clinical and molecular-pathologic data in a transformer-based deep learning model, addressing data heterogeneity and performance generalizability. Me

Externí odkaz: http://arxiv.org/abs/2405.12963

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Multicenter Privacy-Preserving Model Training for Deep Learning Brain Metastases Autosegmentation

Autor: Huang, Yixing, Khodabakhshi, Zahra, Gomaa, Ahmed, Schmidt, Manuel, Fietkau, Rainer, Guckenberger, Matthias, Andratschke, Nicolaus, Bert, Christoph, Tanadini-Lang, Stephanie, Putz, Florian

Publikováno v: Radiotherapy & Oncology. 2024, 198, 110419, 1-8

Objectives: This work aims to explore the impact of multicenter data heterogeneity on deep learning brain metastases (BM) autosegmentation performance, and assess the efficacy of an incremental transfer learning technique, namely learning without for

Externí odkaz: http://arxiv.org/abs/2405.10870

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Akademický článek

EEG-based Incongruency Decoding in AR with sLDA, SVM, and EEGNet

Autor: Wimmer Michael, Veas Eduardo E., Müller-Putz Gernot R.

Publikováno v: Current Directions in Biomedical Engineering, Vol 10, Iss 3, Pp 21-24 (2024)

Augmented reality (AR) technologies enhance a user’s physical environment by providing contextual information about their surroundings. This information might appear incongruent to users, either due to their current mental context or factual errors

Externí odkaz: https://doaj.org/article/afaa4d3da9384b8bba694930460bff73

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A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter Collaboration

Autor: Huang, Yixing, Bert, Christoph, Gomaa, Ahmed, Fietkau, Rainer, Maier, Andreas, Putz, Florian

Due to data privacy constraints, data sharing among multiple clinical centers is restricted, which impedes the development of high performance deep learning models from multicenter collaboration. Naive weight transfer methods share intermediate model

Externí odkaz: http://arxiv.org/abs/2309.17192

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