Self-supervised learning for interventional image analytics: toward robust device trackers-Reference-Cited by-同舟云学术

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Self-supervised learning for interventional image analytics: toward robust device trackers

Published:2024-05-15 Issue:03 Volume:11 Page:
ISSN:2329-4302
Container-title:Journal of Medical Imaging
language:
Short-container-title:J. Med. Imag.

Author:

Islam Saahil¹,Murthy Venkatesh N.²,Neumann Dominik³,Das Badhan Kumar¹,Sharma Puneet²,Maier Andreas¹,Comaniciu Dorin²,Ghesu Florin C.³

Affiliation:

1. Friedrich-Alexander-Universität Erlangen-Nürnberg, Pattern Recognition Lab, Erlangen, Germany

2. Siemens Healthineers, Digital Technology and Innovation, Princeton, New Jersey, United States

3. Siemens Healthineers, Digital Technology and Innovation, Erlangen, Germany

Publisher

SPIE-Intl Soc Optical Eng

Reference53 articles.

1. Dynamic coronary roadmapping via catheter tip tracking in X-ray fluoroscopy with deep learning based Bayesian filtering

2. ESC working group on cardiac cellular electrophysiology position paper: relevance, opportunities, and limitations of experimental models for cardiac electrophysiology research

3. Transarterial chemoembolization: Evidences from the literature and applications in hepatocellular carcinoma patients

4. Dynamic coronary roadmapping during percutaneous coronary intervention: a feasibility study

5. Image-Based Device Tracking for the Co-registration of Angiography and Intravascular Ultrasound Images

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