About

I am a PhD student at the chair of Statistical Learning and Data Science (SLDS) at the Ludwig-Maximilians-Universiät (LMU) München since June 2020. My PhD position at the Department of Statistics is in collaboration with the Klinikum Großhadern - since May 2020 -, which is as well a department of the LMU. As my position there is attached to the "Klinik und Poliklinik für Radiologie" my research focus is on Machine and Deep Learning with radiological data.

I obtained a Bachelor's Degree (B.Sc.) and Master's Degree (M.Sc.) in (Bio-) Statistics at the LMU and already did my Master thesis at the working group Computational Statistics - in cooparation with the Klinikum rechts der Isar.

Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33, D-80539 München

theresa.stueber [at] stat.uni-muenchen.de

&

Klinikum der Universität München

Ludwig-Maximilians-Universität München

Marchioninistr. 15, D-81377 München

theresa.stueber [at] med.uni-muenchen.de

Research Interests

• Medical Data Analysis
• Combination of classical Statistics and Machine Learning
• Deep Learning for Radiological Data
• Benchmark Studies

You Can Find me on

• GitHub
• Google Scholar
• LinkedIn

References

1. 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.
2. 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.
3. Gresser E, Reich J, Stüber AT, Stahl R, Schinner R, Ingrisch M, Peller M, Schroeder I, Kunz WG, Vogel F, Irlbeck M, Ricke J, Puhr-Westerheide D (2022) REDUCE – Indication catalogue based ordering of chest radiographs in intensive care units. Journal of Critical Care 69, 154016.
   link.
4. Gresser E, Schachtner B, Stüber AT, Solyanik O, Schreier A, Huber T, Froelich MF, Magistro G, Kretschmer A, Stief C, Ricke J, Ingrisch M, Nörenberg D (2022) Performance variability of radiomics machine learning models for the detection of clinically significant prostate cancer in heterogeneous MRI datasets. Quantitative Imaging in Medicine and Surgery 12.
   link|pdf.
5. Rueckel J, Fink N, Kaestle S, Stüber T, Schwarze V, Gresser E, Hoppe BF, Rudolph J, Kunz WG, Ricke J, Sabel BO (2021) COVID-19 Pandemic and Upcoming Influenza Season—Does an Expert’s Computed Tomography Assessment Differentially Identify COVID-19, Influenza and Pneumonias of Other Origin? Journal of Clinical Medicine 10.
   link.