Computational Imaging Research Lab | Activities

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. 

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

Today at CVPR 2024: Visual Anomaly Detection Workshop! 

Anomaly detection is crucial for identifying unusual patterns and potential issues in data, making it essential for applications in security, healthcare, and beyond. At the VAND 2.0 Workshop, co-organized by Philipp, top researchers and industry experts are gathering to share insights and innovations in visual anomaly and novelty detection.

Live from CVPR 2024, Seattle, WA
Dive into cutting-edge research, network with fellow professionals, and see how our lab is contributing to transformative advancements in anomaly detection (https://sites.google.com/view/vand-2-0-cvpr-2024/home).

#AnomalyDetection #AI #MachineLearning #CVPR2024 #Research #Innovation #CIRlab

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.

The international ARTEMIS project had its kick-off meeting on February 7th, 2024. It will develop novel technology to integrate machine learning and simulation methods for virtual twins in the diagnosis and treatment of fatty liver disease. 

The Walter Siegenthaler Gesellschaft publishes a position paper with CIR contributions on the topic of "Digitalisierung der Medizin für das Patienten- und Gemeinwohl“.

Workshop co-organized with the US National Academy of Sciences, Engineering and Medicine on Engaging Scientists to Prevent Harmful Exploitation of Advanced Data Analytics and Biological Data.

The CD Lab MLPI will develop novel machine learning methods to assess and predict the response to treatment in lung cancer, and will investigating the bridge of imaging, disease biology and legal environment of using AI in cancer care. (Image: MUW)

June 7th: new paper on prediction of anomalies in high-risk breast cancer women with deep learning by Bianca Burger and colleagues published in European Radiology Experimental.

April 20th 2023: new paper in Nature Communications on evolution of cortical geometry and its link to function, behaviour and ecology in a collaboration of CIR lab, Nathan Kline Institute, Université Paris Cité, University of Liverpool, and Universitat Autònoma de Barcelona.

Feb 17th 2023: new paper on detecting initial in utero effects of prenatal alcohol exposure with help of fetal MRI based brain atlas analysis published by Marlene Stümpflen and colleagues in Cerebral Cortex.

Jan 31st 2023: New paper on fetal brain morphology as a predictor for language development published by Lisa Bartha-Doering in Communications Biology.