Semi-Automatic Rough Classification of Multichannel Medical Imaging Data

2012 ◽  
pp. 71-89 ◽  
Author(s):  
Ahmed Elmoasry ◽  
Mohamed Sadek Maswadah ◽  
Lars Linsen
Keyword(s):  
Medical Imaging ◽  
Imaging Data ◽  
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.

PV Modeling of Medical Imaging Systems

Benford's Law ◽  
2015 ◽  
Author(s):  
John Chiverton ◽  
Kevin Wells
Keyword(s):  
Medical Imaging ◽  
Gold Standard ◽  
Ground Truth ◽  
Imaging Data ◽  
Nuclear Medicine Imaging ◽  
Phantom Imaging ◽  
Positron Emission ◽  
Pet Ct ◽  
Many Sources

This chapter applies a Bayesian formulation of the Partial Volume (PV) effect, based on the Benford distribution, to the statistical classification of nuclear medicine imaging data: specifically Positron Emission Tomography (PET) acquired as part of a PET-CT phantom imaging procedure. The Benford distribution is a discrete probability distribution of great interest for medical imaging, because it describes the probabilities of occurrence of single digits in many sources of data. The chapter thus describes the PET-CT imaging and post-processing process to derive a gold standard. Moreover, this chapter uses it as a ground truth for the assessment of a Benford classifier formulation. The use of this gold standard shows that the classification of both the simulated and real phantom imaging data is well described by the Benford distribution.

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
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