RPS 105 - AI-enabled workflow optimisation in cardiac imaging
Lectures
1
Evaluation of zero-click whole-workflow of CMR cardiac function and strain analyses pipeline on cardio-oncology patients
07:00Gregory Mark Lanza, St. Louis / US
2
A deep learning-based approach to automatically choose a protocol of cardiothoracic CT examinations: understanding the decision process using a rate-distortion framework
07:00Martin Segeroth, Basel / CH
3
Automated segment-level coronary artery calcium scoring on non-contrast CT using deep learning
07:00Bernhard Föllmer, Berlin / DE
4
Diagnostic performance of a deep-learning model to detect coronary stenoses on CTA images in emergency patients presenting with acute chest pain
07:00Carl Guillaume Glessgen, Geneva / CH
5
A direct deep-learning approach for prediction of patient survival in patients undergoing transcatheter aortic valve replacement based on CT data without the need for muscle segmentation
07:00Maike Theis, Cologne / DE
6
Radiomics based on steady-state free precession (SSFP) cine sequences for predicting major adverse cardiac events in patients with dilated cardiomyopathy
11:00Xue Li, Chengdu, Sichuan Province / CN
7
Transfer learning from cine to late gadolinium enhancement MRI for myocardial segmentation in patients with acute myocardial infarction
07:00Saud Ahmad Khan, Paris / FR
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