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Computational Imaging Research Lab (CIR)

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CIR is an interdisciplinary research lab bringing together scientists in machine learning, imaging, medicine, and biology, developing novel methods to understand disease and to improve precision care. 

CIR is a division of the Department of Biomedical Imaging and Image-guided Therapy at the Medical University of Vienna. We are curious about how machine learning has to evolve to make an impact in healthcare. We believe that it has to support treatment decisions, but also needs to advance our understanding of the underlying mechanisms, as in the future it will gain a role in the development of novel treatment strategies. 

CIR is home to a diverse group of international researcher and three PIs leading their research groups in different areas of machine learning, medical imaging and precision medicine. It is closely linked to many clinical disciplines including radiology, oncology, surgery, paediatrics, pathology and neuroscience. We develop methods to predict disease course and treatment response in breast-, and lung cancer, identify novel treatment targets, or model the reorganization of the brain during disease. Sometimes, we perform basic research exploring early brain development, or the evolution of the human brain over the last 77 million years. Lately, we are linking machine learning and simulation in liver disease to bridge the gap between large-scale observations, and mechanistic understanding of disease, and are looking to integrate continually collected fitness tracker data with clinical imaging

Spin-off companies of our members are developing software for clinical radiology, and accurate large-scale clinical annotation campaigns.

CDL Symposium: AI in Cancer Imaging

The AI in Cancer Imaging Symposium brought together AI, Radiology, and Privacy Law in an interdisciplinary discussion joined by Prof. Guillaume Chassagnon from Paris, and Prof. Griet Verhenneman from Ghent.

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Data Science Vienna Poster Workshop

We are happy to be part of the first Data Science Vienna Poster Workshop on Oct 1st 2024 at the Vienna Sternwarte. With over 60 registered participants it will be an inspiring event for the Viennese community.

More Information: https://datascience.univie.ac.at/events/ds-poster-workshop/ 

ECR'24 Pixel Pandemonium

CIR is coorganizing the Pixel Pandemonium at ECR'25. It will host cutting edge machine learning demos in the area of radiology, and bring together the medical imaging, machine learning, and image computing communities. 

It is a collaboration of the European Society of Radiology, the European Institute for Biomedical Imaging Research Joint Initiative on AI for Medical Imaging (AI4MI).

Submit your demo here, until December 2024: https://www.eibir.org/pixelpandemonium/

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AI links phenotype and transcriptomics in brain cancer

New paper by Thomas Roetzer-Pejrimovsky et al. shows how deep learning can map the spatial distribution of transcriptional subtypes and particularly regions in digital pathology data. The results demonstrate how machine learning enables image mining in brain cancer. 

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AI as a cathalyst of integrated diagnostics?

A new editorial reviewing the role of AI in integrated diagnostics, and multidisciplinary team work. Published in Radiology Jul 30th 2024.

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MICCAI 2024 - CIR is co-organizing two workshops in Sep'24

We are thrilled to co-organize two significant events at the international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in Marrakesh on October 6, 2024: the Fetal Brain Tissue Segmentation and Annotation (FeTA) 2024 and the 9th workshop on Perinatal, Preterm, and Pediatric Image Analysis (PIPPI).

For more information on FeTA, visit the FeTA Challenge 2024. Details about PIPPI can be found at the PIPPI Workshop 2024.

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