About

I am a PhD student and research associate at the Chair of Statistical Learning and Data Science at the Ludwig Maximilian University Munich since July 2021. My PhD position is in collaboration with the clinical data science group at the Klinikum Großhadern.

I obtained a Bachelor's degree (B.Sc.) in Business Information Systems from the University of Applied Sciences Ulm and a Master's degree (M.Sc.) in Computer Science from the Ludwig Maximilian University Munich. My research interest is in the practical application of Machine & Deep Learning in the medical domain. I am particularly interested in representation learning and generative models.

Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

tobias.weber [at] stat.uni-muenchen.de

Research Interests

• Application of deep learning in medicine & computer vision
• Deep learning for radiological data

You Can Find me on

• GitHub
• LinkedIn

References

1. Weber T, Ingrisch M, Bischl B, Rügamer D (2024) Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV),
   link|pdf.
2. Weber T, Ingrisch M, Bischl B, Rügamer D (2023) Unreading Race: Purging Protected Features from Chest X-ray Embeddings. arXiv:2311.01349.
   link|pdf.
3. Jeblick K, Schachtner B, Dexl J, Mittermeier A, Stüber AT, Topalis J, Weber T, Wesp P, Sabel B, Ricke J, Ingrisch M (2023) ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reports. European Radiology.
   link|pdf.
4. Bothmann L, Wimmer L, Charrakh O, Weber T, Edelhoff H, Peters W, Nguyen H, Benjamin C, Menzel A (2023) Automated wildlife image classification: An active learning tool for ecological applications. Ecological Informatics 77.
   link|pdf.
5. Stüber AT, Coors S, Schachtner B, Weber T, Rügamer D, Bender A, Mittermeier A, Öcal O, Seidensticker M, Ricke J, others (2023) A Comprehensive Machine Learning Benchmark Study for Radiomics-Based Survival Analysis of CT Imaging Data in Patients With Hepatic Metastases of CRC. Investigative Radiology, 10–1097.
6. Weber T, Ingrisch M, Bischl B, Rügamer D (2023) Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis Advances in Knowledge Discovery and Data Mining: 27th Pacific-Asia Conference, PAKDD 2023,
   link|pdf.
7. Weber T, Ingrisch M, Bischl B, Rügamer D (2023) Implicit Embeddings via GAN Inversion for High Resolution Chest Radiographs. MICCAI Workshop on Medical Applications with Disentanglements 2022.
   link|pdf.
8. Weber T, Ingrisch M, Fabritius M, Bischl B, Rügamer D (2021) Survival-oriented embeddings for improving accessibility to complex data structures. NeurIPS 2021 Workshops, Bridging the Gap: From Machine Learning Research to Clinical Practice.
   link|pdf.
9. Weber T, Ingrisch M, Bischl B, Rügamer D (2021) Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation. NeurIPS 2021 Workshops, Deep Generative Models and Downstream Applications.
   link|pdf.