Applied Medical Image & Data Analysis Lab
Welcome to the website of our research group
Lab Maros & Wenz
Introduction & Mission statement
In medicine we generate vast amounts of data on a daily basis. However, the manner in which it is obtained is, for the most part, unstructured and therefore rarely ready to be utilized to retrieve valuable insights or synthesize new knowledge. Text and Images are the two classical outputs/pillars of diagnostic medical (especially radiological) data. Furthermore, high-throughput genomic assay technologies are becoming indispensable in state-of-the-art diagnostic and therapeutic decision-making.
The objective of our new research group is to combine these facets of information and exploit the recent advancement and renaissance of machine learning and deep learning algorithms with the aim to improve the accuracy and depth of radiological diagnostic processes as well as the quality and information content of our reports.
Through such efforts, our ultimate goal (and hope) is to work towards safer personalized and more effective patient care while strengthening the role of radiologists in the future.
106th Annual Meeting of the Radiological Society of North America (RSNA)
Past Events >
> 54. Jahrestagung der Deutschen Gesellschaft für Neuroradiologie e.V.
The hidden prowess of NLP and radiological reports: a text mining pipeline for machine learning-assisted diagnostics and reportingJunge, AG, Cho GC , Saase, V, Kämpgen B, Groden C, Ganslandt T, Wenz H, Maros ME.
Oral presentation. Forschung: Zerebrovaskulär und anderes, 8:30-10:00, 10/12/2019
How to break the bottleneck of AI driven radiology and get your hands on the new gold: "RIPE" a solution for data extraction and analysis pipelineJunge, AG, Cho GC , Saase, V, Kämpgen B, Förster A, Böhme J, Groden C, Ganslandt T, Wenz H, Maros ME.
Poster Presentation. Posterbegehungen IX (KI & Neuroonkologie), 8:30-9:30, 10/12/2019
**Breaking news: the work received the poster prize of the KI & Neuroonkologie poster session.**
In cooperation with the Experimental Neuroradiology and preclinical Imaging Group/AG
Longitudinal in vivo evaluation of large vessel vasospasm in mice using a micro-CTWeyer V, Maros ME, Kronfeld A, Kirschner S, Groden C, Sommer C, Tanyildizi Y, Kramer M, Brockmann MA.
Poster Presentation. Posterbegehung II (Interventional II), 14:15 - 15:15, 10/10/2019
> 57th American Society of Neuroradiology (ASNR) Annual Meeting, Boston, MA, USA
Deep learning-based imaging classifier for improving differential diagnosis between primary and metastatic brain tumors with highly overlapping MRI morphologiesME Maros, CG Cho, A Förster, A von Deimling, M Seiz-Rosenhagen, D Hänggi, Christoph Groden, Holger Wenz Oral presentation, ASNR Boston, May 20, 2019.
Quality assessment of structured multi-parametric MRI reports of the prostate based on RADLEX mapping of urosurgical key information contentME Maros, F Siegel, B Kämpgen, P Sodmann, W Sommer, SO Schönberg, T Henzler, C Groden, H Wenz 7788, Imaging Informatics - SS 205 - Intelligent dose and quality management, 2/27 2019; Link.
> DGNR 2018, Frankfurt am Main, Germany
Deep learning-based imaging classifier for improving differential diagnosis between primary and metastatic brain tumorsME Maros, CG Cho, A Förster, A von Deimling, M Seiz-Rosenhagen, D Hänggi, Christoph Groden, Holger Wenz Oral presentation & Poster - Künstliche Intelligenz: Big Data, Deutsche Gesellschaft f. Neuroradiologie (DGNR), 10/6 2018; Link.
> ASNR 2018, Vancouver, BC, Canada
Real-time decision support for cerebral vasospasm detection on conventional angiograms using deep learningM Maros, A Förster, M Alzghloul, E Neumaier-Probst, C Cho, J Böhme, C Groden, H Wenz O-283, Parallel Paper Session: Deep Learning Techniques and Methods 15E, 6/5 2018, 3:00pm-4:30pm ; Link.
Structured reporting supports junior readers and improves PI-RADS conformity of multi-parametric MRI reports of the prostate-based on cross-lingual RADLEX annotationsM.E. Maros, B. Kämpgen, A. Förster, C. Groden, W.H. Sommer, S.O. Schönberg, T. Henzler, H. Wenz B-1582, Scientific Sesion - SS 1905 - Structured reporting and CAD, 3/4 2018; Link.
Opportunities in automatically annotating radiology reports with RadLex termsB. Kämpgen, M.E. Maros, T. Stening, W.H. Sommer, A. Klüter B-1583, Scientific Sesion - SS 1905 - Structured reporting and CAD, 3/4 2018; Link.
Online Webtool für objektive Vergleichbarkeit klassischer Befundtexte und semiautomatisch angefertigter strukturierter Befunde;Máté E. Maros, Matthias Fröhlich, Benedikt Kämpgen, Alex Förster, Christoph Groden Wieland Sommer, Stefan Schönberg, Thomas Henzler, Holger Wenz 367, Poster session - Computer science, Deutsche Gesellschaft f. Neuroradiologie (DGNR), 11/04-08 2017; Link.
This work received the Poster Prize in Computer Science section @ The 52nd Annual Congress (2017) of the German Society for Neuroradiology (DGNR)
Academic | Federal Cooperations & Fundings
The German Federal Ministry for Economic Affairs and Energy ( BMWi ) funds innovative research projects through its ( Zentrales Innovationsprogramm Mittelstand, ZIM ) project to foster cooperation between academia and industry. We are currently currently working on such a BMWi-ZIM Project in partnership with EMPOLIS GmbH (see below and under <Projects> ).
is a joint (consortial) project including eight German universities that have agreed to share data to enhance patient-centered research and clinical care. It is funded by the German Ministry for Education and Research (BMBF) within the framework of the Medical Informatics Initiative (MI-I, 2015) to foster IT innovations for healthcare research and medical care. Our research group is involved in the MIRACUM Network with regard to Neurovascular and Neurooncological Use Cases.
Industrial Partners & Cooperations
EMPOLIS has long-lasting and extensive experience in information management. We have a working cooperation with the Healthcare Analytics Branch of EMPOLIS with Dr. Benedikt Kämpgen (Head of SU Healthcare). Our cooperation has recently been awarded federal funding by the BMWi's ZIM (see above) for a two-year time span to develop software solutions for improving the radiological reporting process. We are currently focusing on identifying automatable objective quality measures for reporting.
Smart-Radiology provides a set of tools, developed explicitly with radiologists in mind, that allows for creating highly task- and institution-specific reporting templates. In cooperation with SR, our research group has developed stroke- and RANO reporting templates. Furthermore, we have been investigating the effects that such structured reporting tools have on report quality while trying to find objective, scalable ways to compare report quality.
Department of Neuroradiology
University Medical Center