Clinical Data Science
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Clinical Data Science in Radiology
ML & AI for the Clinics: Interdisciplinary, Trustworthy, Explainable
Research Areas
Radiological Computer Vision: Applying established machine-learning-based computer vision techniques for radiological diagnosis and prognosis
Biostatistics: Applying statistical methods to analyze and interpret complex clinical data with statistical rigorosity
Applied LLMs: Using large language models to process and understand numerous aspects of clinical text data
Multimodal DL: Integrating various data types (imaging, text, etc.) to enhance the accuracy and applicability of our AI models
Our Data
We work with real-world radiological data from clinical routine in a large radiology department. Our data comprises imaging data from the humble chest X-ray over computed tomography imaging and magnetic resonance imaging to combined functional and morphological imaging with PET/CT. Imaging data is complemented by comprehensive metadata, including free-text radiological reports.
Selected Projects
Image analysis of PET/CT: We are organizers of the AutoPET Challenge series, which focuses on segmentation of pathological tracer uptake in hybrid imaging.
High Performance Computing (HPC): We maintain the clinical HPC core facility CORE - for heavy CPU and GPU workloads. Our computing enviornment enables local deployment of state-of-the-art machine learning models.
Clinical LLM: Developing language models tailored for clinical data analysis.
Reliable AI: Clinical AI applications require careful evaluation with a strong focus on reliability, explainability and trustworthiness - does the model predict the right thing for the right reasons?
Federated Learning (RACOON, BORN): We contribute to collaborative multi-centric radiology frameworks that enable clinical machine learning and ensure data privacy and security.
About Us
We are an interdisciplinary research group for clinical data science in radiology. Our mission is to advance clinical data science to improve patient outcomes by leveraging cutting-edge AI technologies, including computer vision, applied large language models (LLMs), and multimodal deep learning (DL). Our diverse team, including specialists from physics, statistics, computer science, and various clinical fields, collaborates closely with radiologists to create innovative solutions for real-world healthcare challenges.
Join Our Group
Are you a PhD candidate in biostatistics or computer science? Do you want to be part of a dynamic, interdisciplinary team that's shaping the future of clinical data science? We invite you to explore the opportunities we offer. Here, you will work on impactful projects, engage with real clinical data, and contribute to the development of AI tools that are reliable, explainable, and trustworthy.
- Work on cutting-edge AI projects
- Collaborate with a diverse team of experts
- Engage with real clinical data
- Contribute to impactful healthcare solutions
Contact
Prof. Dr. Michael Ingrisch
Head of Clinical Data Science Josef Lissner Laboratorium / EG00 / Würfel KL
Marchioninistraße 15+49 89 4400 44602
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The Clinical Data Science group gratefully acknowledges research funding by:
Bavarian Research Alliance (BayFOR)
Brückner Bachmann Leipold Foundation
Bundesministerium für Gesundheit
Deutsches Zentrum für Lungenforschung (DZL)
Munich Center for Machine Learning (MCML)
RACOON, Netzwerk Universitätsmedizin
relAI! – Konrad Zuse School of Excellence in Reliable AI
Siemens Healthineers
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Group Leader
Prof. Dr. rer. nat. Michael Ingrisch
Head of Clinical Data Science.
Research Staff
Dr. rer. nat. Katharina Jeblick, MA phil.
Katharina received her Phd in Physics in the field of computational physics for quantum materials and holds an additional master degree in Philosophy of Science and Technology focussing on ethical and social implications of technology.
Her postdoctoral research scope includes the application and advancement of deep learning for lung imaging to support clinical decision making.
Dr. rer. nat. Andreas Mittermeier
Andreas received his PhD in the field of medical physics for his research on novel perfusion imaging analysis. His postdoctoral research focuses on natural language processing applications in radiology.
Dr. rer. nat. Balthasar Schachtner
Balthasar received his PhD for his research in the field of experimental particle physics. The focus of his postdoctoral research is the facilitation of machine learning in radiology and the development of imaging biomarkers for lung pathologies.
Dr. rer. nat. Philipp Wesp
Philipp is a physicist working on machine learning-based medical image analysis. His research interests include multi-modal machine learning and the role of explainable machine learning algorithms in radiology. During his PhD he focused on applying traditional deep learning methods for differentiating benign from malignant lesions and chronological age prediction using computed tomography scans.
PhD Students
Jakob Dexl, M.Sc.
Jakob has a Master's degree in medical engineering with a focus on image and data processing. Before joining the group, he worked as a research assistant at the Fraunhofer IIS and applied various deep learning approaches to pathological tasks. In his doctoral research, he is developing active learning algorithms for radiology.
Timo Löhr, M.Sc.
Timo has a master's degree in computer science with a focus on data analysis and machine learning. The scope of his doctoral research project includes the application and enhancement of machine learning algorithms for large medical data sets.
Theresa Stüber, M.Sc.
Theresa got her master's degree in (bio-)statistics and pursued her great interest for machine learning in medicine already during university studies. In her doctoral research she develops a framework for the combinaton of deep learning with classical statistical modeling.
Johanna Topalis, M.Sc.
Johanna has a Master's degree in physics and specialized in medical physics during her studies. Her doctoral research project concerns the development of AI-based tools to support decision-making in lung cancer screening.
Master Students, Medical Doctoral Students and Interns
Leon Orasanin, B.Sc.
Leon is a medical doctoral student. He focuses on machine learning applications in cancer-staging and -characterisation based on PET/CTs. He also holds a B.Sc. in Management & Technology.
Felipe Sanches Saviolli
Felipe is a medical student pursuing a doctoral thesis on machine learning-based medical image analysis. His research focuses on the detection of sarcopenia using deep learning algorithms to analyze chest X-ray images. Felipe’s interests lie at the intersection of medical imaging and artificial intelligence, particularly in developing accessible tools for early diagnosis and intervention.
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Nicolas Stelzer
Nicolas is an artificial intelligence student researching the usability of large language models in medicine. As part of his internship, he is focusing on a project that explores advanced techniques for medical entity extraction using LLMs.
Yuzhuo Wang, B.Sc.
Master student.
Medical Advisor Radiology
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2024
Gatidis S, Früh M, Fabritius MP, Gu S, Nikolaou S, Fougère CL, Ye J, He J, Peng Y, Bi L, Ma J, Wang B, Zhang J, Huang Y, Heiliger L, Marinov Z, Stiefelhagen R, Egger J, Kleesiek J, Sibille L, Xiang L, Bendazzoli S, Astaraki M, Ingrisch M, Cyran CC, Küstner T. Results from the autoPET challenge on fully automated lesion segmentation in oncologic PET/CT imaging. Nature Machine Intelligence. 2024 Oct 30. doi: 10.1038/s42256-024-00912-9.
Heimer MM, Dikhtyar Y, Hoppe BF, Herr FL, Stüber AT, Burkard T, Zöller E, Fabritius MP, Unterrainer L, Adams L, Thurner A, Kaufmann D, Trzaska T, Kopp M, Hamer O, Maurer K, Ristow I, May MS, Tufman A, Spiro J, Brendel M, Ingrisch M, Ricke J, Cyran CC. Software-assisted structured reporting and semi-automated TNM classification for NSCLC staging in a multicenter proof of concept study. Insights Imaging. 2024 Oct 28;15(1):258. doi: 10.1186/s13244-024-01836-z. PMID: 39466506; PMCID: PMC11519274.
Heldring N, Rezaie AR, Larsson A, Gahn R, Zilg B, Camilleri S, Saade A, Wesp P, Palm E, Kvist O. A probability model for estimating age in young individuals relative to key legal thresholds: 15, 18 or 21-year. Int J Legal Med. 2024 Sep 18. doi: 10.1007/s00414-024-03324-x. Epub ahead of print. PMID: 39292274.
Portafaix A, Reidler P, Sabel B, Dexl J, Jeblick K, Mittermeier A, Ingrisch M, Fevens T. Computer vision-based guidance tool for correct radiographic hand positioning. Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment. 2024 Feb 18-23. doi: 10.1117/12.3005807
Wesp P, Schachtner BM, Jeblick K, Topalis J, Weber M, Fischer F, Penning R, Ricke J, Ingrisch M, Sabel BO. Radiological age assessment based on clavicle ossification in CT: enhanced accuracy through deep learning. Int J Legal Med. 2024 Jan 30. doi: 10.1007/s00414-024-03167-6. Epub ahead of print. PMID: 38286953.
2023
Feuerecker B, Heimer MM, Geyer T, Fabritius MP, Gu S, Schachtner B, Beyer L, Ricke J, Gatidis S, Ingrisch M, Cyran CC. Artificial Intelligence in Oncological Hybrid Imaging. Nuklearmedizin. 2023 Oct;62(5):296-305. English. doi: 10.1055/a-2157-6810. Epub 2023 Oct 6. PMID: 37802057.
Jeblick K, Schachtner B, Dexl J, Mittermeier A, Stüber AT, Topalis J, Weber T, Wesp P, Sabel BO, Ricke J, Ingrisch M. ChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports. Eur Radiol. 2023 Oct 5. doi: 10.1007/s00330-023-10213-1. PMID: 37794249.
Mansour N*, Mittermeier A*, Walter R, Schachtner B, Rudolph J, Erber B, Schmidt VF, Heinrich D, Bruedgam D, Tschaidse L, Nowotny H, Bidlingmaier M, Kunz SL, Adolf C, Ricke J, Reincke M, Reisch N, Wildgruber M*, Ingrisch M*. Integration of clinical parameters and CT-based radiomics improves machine learning assisted subtyping of primary hyperaldosteronism. Frontiers in Endocrinology. 2023 August Aug 24. doi: 10.3389/fendo.2023.1244342.
Stüber AT, Coors S, Schachtner B, Weber T, Rügamer D, Bender A, Mittermeier A, Öcal O, Seidensticker M, Ricke J, Bischl B, Ingrisch M. A Comprehensive Machine Learning Benchmark Study for Radiomics-Based Survival Analysis of CT Imaging Data in Patients With Hepatic Metastases of CRC. Invest Radiol. 2023 Jul 28. doi: 10.1097/RLI.0000000000001009. PMID: 37504498.
Weber T, Ingrisch M, Bischl B, Rügamer D. Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis. In: Advances in Knowledge Discovery and Data Mining: 27th Pacific-Asia Conference, PAKDD 2023. 2023.
Wesp P, Sabel BO, Mittermeier A, Stüber AT, Jeblick K, Schinke P, Mühlmann M, Fischer F, Penning R, Ricke J, Ingrisch M, Schachtner BM. Automated localization of the medial clavicular epiphyseal cartilages using an object detection network: a step towards deep learning-based forensic age assessment. Int J Legal Med. 2023 Feb 2. doi: 10.1007/s00414-023-02958-7. PMID: 36729183.
Weber T, Ingrisch M, Bischl B, Rügamer D. Implicit Embeddings via GAN Inversion for High Resolution Chest Radiographs. In: Medical Applications with Disentanglements. MAD 2022. Lecture Notes in Computer Science, vol 13823. Springer, Cham. 2023 Feb 1. doi: 10.1007/978-3-031-25046-0_3
Dietrich O, Cai M, Tuladhar AM, Jacob MA, Drenthen GS, Jansen JFA, Marques JP, Topalis J, Ingrisch M, Ricke J, de Leeuw FE, Duering M, Backes WH. Integrated intravoxel incoherent motion tensor and diffusion tensor brain MRI in a single fast acquisition. NMR Biomed. 2023 Jan 13:e4905. doi: 10.1002/nbm.4905. Epub ahead of print. PMID: 36637237.
2022
Jeblick K, Schachtner B, Dexl J, Mittermeier A, Stüber AT, Topalis J, Weber T, Wesp P, Sabel B, Ricke J, Ingrisch M. ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reports. arXiv preprint. 2022 Dec;30. doi: 10.48550/arXiv.2212.14882. arxiv: 2212.14882
Wiltgen T, Fleischmann DF, Kaiser L, Holzgreve A, Corradini S, Landry G, Ingrisch M, Popp I, Grosu AL, Unterrainer M, Bartenstein P, Parodi K, Belka C, Albert N, Niyazi M, Riboldi M. 18F-FET PET radiomics-based survival prediction in glioblastoma patients receiving radio(chemo)therapy. Radiat Oncol. 2022 Dec 2;17(1):198. doi: 10.1186/s13014-022-02164-6. PMID: 36461120; PMCID: PMC9719240.
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. Performance variability of radiomics machine learning models for the detection of clinically significant prostate cancer in heterogeneous MRI datasets. Quant Imaging Med Surg. 2022 Nov;12(11):4990-5003. doi: 10.21037/qims-22-265. PMID: 36330197; PMCID: PMC9622454.
Feuerecker B, Heimer MM, Geyer T, Fabritius MP, Gu S, Schachtner B, Beyer L, Ricke J, Gatidis S, Ingrisch M, Cyran CC. Artificial Intelligence in Oncological Hybrid Imaging. Rofo. 2022 Sep 28. English. doi: 10.1055/a-1909-7013. PMID: 36170852.
Wesp P, Schachtner B, Grosu S, Mittermeier A, Stueber A, Stadler K, Cyran C, Ricke J, Ingrisch M. Allowing machine learning models to say “I don’t know”: Improving automated clinical decision-making by balancing performance against abstention. ECR 2022. doi: 10.26044/ecr2022/C-14829
Nadjiri J, Schachtner B, Bücker A, Heuser L, Morhard D, Mahnken AH, Hoffmann RT, Berlis A, Katoh M, Reimer P, Ingrisch M, Paprottka PM, Landwehr P. Nationwide Provision of Radiologically-guided Interventional Measures for the Supportive Treatment of Tumor Diseases in Germany - An Analysis of the DeGIR Registry Data. Rofo. 2022 Sep;194(9):993-1002. English, German. doi: 10.1055/a-1735-3615. Epub 2022 Mar 10. PMID: 35272356.
Radosa CG, Nadjiri J, Mahnken AH, Bücker A, Heuser LJ, Morhard D, Landwehr P, Berlis A, Katoh M, Reimer P, Schachtner B, Ingrisch M, Paprottka P, Hoffmann RT. Availability of Interventional Oncology in Germany in the Years 2018 and 2019 - Results from a Nationwide Database (DeGIR Registry Data). Rofo. 2022 Jul;194(7):755-761. English, German. doi: 10.1055/a-1729-0951. Epub 2022 Feb 24. PMID: 35211926.
Matic A, Monnet M, Lorenz JM, Schachtner B, Messerer T. Quantum-classical convolutional neural networks in radiological image classification. arXiv. 2022 Apr. doi: 10.48550/ARXIV.2204.12390
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. REDUCE - Indication catalogue based ordering of chest radiographs in intensive care units. J Crit Care. 2022 Jun;69:154016. doi: 10.1016/j.jcrc.2022.154016. Epub 2022 Mar 10. PMID: 35279494.
Mittermeier A, Reidler P, Fabritius MP, Schachtner B, Wesp P, Ertl-Wagner B, Dietrich O, Ricke J, Kellert L, Tiedt S, Kunz WG, Ingrisch M. End-to-End Deep Learning Approach for Perfusion Data: A Proof-of-Concept Study to Classify Core Volume in Stroke CT. Diagnostics (Basel). 2022 May 5;12(5):1142. doi: 10.3390/diagnostics12051142. PMID: 35626298; PMCID: PMC9139580.
Öcal O, Ingrisch M, Ümütlü MR, Peynircioglu B, Loewe C, van Delden O, Vandecaveye V, Gebauer B, Zech CJ, Sengel C, Bargellini I, Iezzi R, Benito A, Pech M, Malfertheiner P, Ricke J, Seidensticker M. Prognostic value of baseline imaging and clinical features in patients with advanced hepatocellular carcinoma. Br J Cancer. 2022 Feb;126(2):211-218. doi: 10.1038/s41416-021-01577-6. Epub 2021 Oct 22. PMID: 34686780.
Wesp P, Grosu S, Graser A, Maurus S, Schulz C, Knösel T, Fabritius MP, Schachtner B, Yeh BM, Cyran CC, Ricke J, Kazmierczak PM, Ingrisch M. Deep learning in CT colonography: differentiating premalignant from benign colorectal polyps. European Radiology. 2022 Jan. doi: 10.1007/s00330-021-08532-2
2021
Weber T, Ingrisch M, Fabritius M, Bischl B, Rügamer D. Survival-oriented embeddings for improving accessibility to complex data structures. NeurIPS 2021 Bridging the Gap: From Machine Learning Research to Clinical Practice. 2021 Oct 28;abs/2110.11303. DBLP: https://dblp.uni-trier.de/rec/journals/corr/abs-2110-11303.html
Weber T, Ingrisch M, Bischl B, Rügamer D. Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation. NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications. 2021 Oct 19;abs/2110.11312. DBLP: https://dblp.uni-trier.de/rec/journals/corr/abs-2110-11312.html
Fabritius MP, Seidensticker M, Rueckel J, Heinze C, Pech M, Paprottka KJ, Paprottka PM, Topalis J, Bender A, Ricke J, Mittermeier A, Ingrisch M. Bi-Centric Independent Validation of Outcome Prediction after Radioembolization of Primary and Secondary Liver Cancer. Journal of Clinical Medicine. 2021 Aug 19. doi: 10.3390/jcm10163668
Mahnken AH, Nadjiri J, Schachtner B, Bücker A, Heuser LJ, Morhard D, Landwehr P, Hoffmann RT, Berlis A, Katoh M, Reimer P, Ingrisch M, Paprottka P. Availability of interventional-radiological revascularization procedures in Germany - an analysis of the DeGIR Registry Data 2018/19. Rofo. 2021 Aug 4. English, German. doi: 10.1055/a-1535-2774. Epub ahead of print. PMID: 34348401.
Grosu S, Wesp P, Graser A, Maurus S, Schulz C, Knösel T, Cyran CC, Ricke J, Ingrisch M, Kazmierczak PM. Machine Learning-based Differentiation of Benign and Premalignant Colorectal Polyps Detected with CT Colonography in an Asymptomatic Screening Population: A Proof-of-Concept Study. Radiology. 2021 Feb 23;202363. doi: 10.1148/radiol.2021202363. Epub ahead of print. PMID: 33620287.
2020
Rueckel J, Fink N, Kaestle S, Stüber T, Schwarze V, Gresser E, Hoppe BF, Rudolph J, Kunz WG, Ricke J, Sabel BO. COVID-19 Pandemic and Upcoming Influenza Season-Does an Expert's Computed Tomography Assessment Differentially Identify COVID-19, Influenza and Pneumonias of Other Origin? J Clin Med. 2020 Dec 28;10(1):84. doi: 10.3390/jcm10010084. PMID: 33379386; PMCID: PMC7795488.
Scherer J, Nolden M, Kleesiek J, Metzger J, Kades K, Schneider V, Bach M, Sedlaczek O, Bucher AM, Vogl TJ, Grünwald F, Kühn JP, Hoffmann RT, Kotzerke J, Bethge O, Schimmöller L, Antoch G, Müller HW, Daul A, Nikolaou K, la Fougère C, Kunz WG, Ingrisch M, Schachtner B, Ricke J, Bartenstein P, Nensa F, Radbruch A, Umutlu L, Forsting M, Seifert R, Herrmann K, Mayer P, Kauczor HU, Penzkofer T, Hamm B, Brenner W, Kloeckner R, Düber C, Schreckenberger M, Braren R, Kaissis G, Makowski M, Eiber M, Gafita A, Trager R, Weber WA, Neubauer J, Reisert M, Bock M, Bamberg F, Hennig J, Meyer PT, Ruf J, Haberkorn U, Schoenberg SO, Kuder T, Neher P, Floca R, Schlemmer HP, Maier-Hein K. Joint Imaging Platform for Federated Clinical Data Analytics. JCO Clin Cancer Inform. 2020 Nov;4:1027-1038. doi: 10.1200/CCI.20.00045. PMID: 33166197; PMCID: PMC7713526.
Rueckel J, Kunz WG, Hoppe BF, Patzig M, Notohamiprodjo M, Meinel FG, Cyran CC, Ingrisch M, Ricke J, Sabel BO. Artificial Intelligence Algorithm Detecting Lung Infection in Supine Chest Radiographs of Critically Ill Patients With a Diagnostic Accuracy Similar to Board-Certified Radiologists. Crit Care Med. 2020 Jul;48(7):e574-e583. doi: 10.1097/CCM.0000000000004397. PMID: 32433121.
Rueckel J, Trappmann L, Schachtner B, Wesp P, Hoppe BF, Fink N, Ricke J, Dinkel J, Ingrisch M, Sabel BO. Impact of Confounding Thoracic Tubes and Pleural Dehiscence Extent on Artificial Intelligence Pneumothorax Detection in Chest Radiographs. Invest Radiol. 2020 Jul 15. doi: 10.1097/RLI.0000000000000707. Epub ahead of print. PMID: 32694453.
Nadjiri J, Schachtner B, Bücker A, Heuser L, Morhard D, Landwehr P, Mahnken A, Hoffmann RT, Berlis A, Katoh M, Reimer P, Ingrisch M, Paprottka PM. Availability of Transcatheter Vessel Occlusion Performed by Interventional Radiologists to Treat Bleeding in Germany in the Years 2016 and 2017 - An Analysis of the DeGIR Registry Data. Rofo. 2020 Oct;192(10):952-960. English, German. doi: 10.1055/a-1150-8087. Epub 2020 Jul 7. PMID: 32634837.
2019
Mittermeier A, Ertl-Wagner B, Ricke J, Dietrich O, Ingrisch M. Bayesian pharmacokinetic modeling of dynamic contrast-enhanced magnetic resonance imaging: validation and application. Phys Med Biol. 2019 Sep 17;64(18):18NT02. doi: 10.1088/1361-6560/ab3a5a
Fasler DA, Ingrisch M, Nanz D, Weckbach S, Kyburz D, Fischer DR, Guggenberger R, Andreisek G. Rheumatoid cervical pannus: feasibility of volume and perfusion quantification using dynamic contrast enhanced time resolved MRI. Acta Radiol. 2020 Feb;61(2):227-235. doi: 10.1177/0284185119854200. Epub 2019 Jun 6. PMID: 31169411.
Smith EE, Biessels GJ, De Guio F, de Leeuw FE, Duchesne S, Düring M, Frayne R, Ikram MA, Jouvent E, MacIntosh BJ, Thrippleton MJ, Vernooij MW, Adams H, Backes WH, Ballerini L, Black SE, Chen C, Corriveau R, DeCarli C, Greenberg SM, Gurol ME, Ingrisch M, Job D, Lam BYK, Launer LJ, Linn J, McCreary CR, Mok VCT, Pantoni L, Pike GB, Ramirez J, Reijmer YD, Romero JR, Ropele S, Rost NS, Sachdev PS, Scott CJM, Seshadri S, Sharma M, Sourbron S, Steketee RME, Swartz RH, van Oostenbrugge R, van Osch M, van Rooden S, Viswanathan A, Werring D, Dichgans M, Wardlaw JM. Harmonizing brain magnetic resonance imaging methods for vascular contributions to neurodegeneration. Alzheimers Dement (Amst). 2019 Feb 26;11:191-204. doi: 10.1016/j.dadm.2019.01.002. PMID: 30859119; PMCID: PMC6396326.
Debus C, Floca R, Ingrisch M, Kompan I, Maier-Hein K, Abdollahi A, Nolden M. MITK-ModelFit: A generic open-source framework for model fits and their exploration in medical imaging - design, implementation and application on the example of DCE-MRI. BMC Bioinformatics. 2019 Jan 16;20(1):31. doi: 10.1186/s12859-018-2588-1. PMID: 30651067; PMCID: PMC6335810.
Suchorska B, Schüller U, Biczok A, Lenski M, Albert NL, Giese A, Kreth FW, Ertl-Wagner B, Tonn JC, Ingrisch M. Contrast enhancement is a prognostic factor in IDH1/2 mutant, but not in wild-type WHO grade II/III glioma as confirmed by machine learning. Eur J Cancer. 2019
