Towards the generation of dry patient‐specific human aortic valve models from medical imaging data

PAMM ◽  
2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Sergio Morales Ortuno ◽  
Oliver Röehrle
Keyword(s):  
Medical Imaging ◽  
Aortic Valve ◽  
Patient Specific ◽  
Imaging Data ◽  
Human Aortic Valve ◽  
Medical Imaging Data

Nearest Neighbor-Based Strategy to Optimize Multi-View Triplet Network for Classification of Small-Sample Medical Imaging Data

2021 ◽  
pp. 1-15
Author(s):  
Phawis Thammasorn ◽  
Wanpracha A. Chaovalitwongse ◽  
Daniel S. Hippe ◽  
Landon S. Wootton ◽  
Eric C. Ford ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Nearest Neighbor ◽  
Small Sample ◽  
Imaging Data ◽  
Medical Imaging Data

Augmented and mixed reality for collaborative cardiac planning using 3D and 4D medical imaging data

2020 ◽  
Author(s):  
Didi-Liliana Popa ◽  
Mihai-Lucian Mocanu ◽  
Radu-Teodoru Popa ◽  
Lucian-Florentin Barbulescu ◽  
Linda Nicoleta Barbulescu ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Mixed Reality ◽  
Imaging Data ◽  
Medical Imaging Data

scholarly journals A framework for secure and decentralized sharing of medical imaging data via blockchain consensus

2018 ◽  
Vol 25 (4) ◽  
pp. 1398-1411 ◽  
Author(s):  
Vishal Patel
Keyword(s):  
Medical Imaging ◽  
Large Scale ◽  
Third Party ◽  
Distributed Data ◽  
Imaging Data ◽  
Privacy And Security ◽  
Image Sharing ◽  
Security Models ◽  
Number Of Factors ◽  
Medical Imaging Data

The electronic sharing of medical imaging data is an important element of modern healthcare systems, but current infrastructure for cross-site image transfer depends on trust in third-party intermediaries. In this work, we examine the blockchain concept, which enables parties to establish consensus without relying on a central authority. We develop a framework for cross-domain image sharing that uses a blockchain as a distributed data store to establish a ledger of radiological studies and patient-defined access permissions. The blockchain framework is shown to eliminate third-party access to protected health information, satisfy many criteria of an interoperable health system, and readily generalize to domains beyond medical imaging. Relative drawbacks of the framework include the complexity of the privacy and security models and an unclear regulatory environment. Ultimately, the large-scale feasibility of such an approach remains to be demonstrated and will depend on a number of factors which we discuss in detail.

Deep Random Forests for Small Sample Size Prediction with Medical Imaging Data

2020 ◽  
Author(s):  
Alexander Katzmann ◽  
Alexander Muehlberg ◽  
Michael Suehling ◽  
Dominik Norenberg ◽  
Julian Walter Holch ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Sample Size ◽  
Random Forests ◽  
Small Sample Size ◽  
Small Sample ◽  
Imaging Data ◽  
Medical Imaging Data
Sign in / Sign up

Export Citation Format

Share Document