Federated Learning for Coronary Artery Plaque Detection in Atherosclerosis Using IVUS Imaging: A Multi-Hospital Collaboration
में बचाया:
| में प्रकाशित: | arXiv.org (Dec 19, 2024), p. n/a |
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| मुख्य लेखक: | |
| अन्य लेखक: | , , |
| प्रकाशित: |
Cornell University Library, arXiv.org
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| विषय: | |
| ऑनलाइन पहुंच: | Citation/Abstract Full text outside of ProQuest |
| टैग: |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3148681740 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3148681740 | ||
| 045 | 0 | |b d20241219 | |
| 100 | 1 | |a Chiu-Han, Hsiao | |
| 245 | 1 | |a Federated Learning for Coronary Artery Plaque Detection in Atherosclerosis Using IVUS Imaging: A Multi-Hospital Collaboration | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 19, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a The traditional interpretation of Intravascular Ultrasound (IVUS) images during Percutaneous Coronary Intervention (PCI) is time-intensive and inconsistent, relying heavily on physician expertise. Regulatory restrictions and privacy concerns further hinder data integration across hospital systems, complicating collaborative analysis. To address these challenges, a parallel 2D U-Net model with a multi-stage segmentation architecture has been developed, utilizing federated learning to enable secure data analysis across institutions while preserving privacy. The model segments plaques by identifying and subtracting the External Elastic Membrane (EEM) and lumen areas, with preprocessing converting Cartesian to polar coordinates for improved computational efficiency. Achieving a Dice Similarity Coefficient (DSC) of 0.706, the model effectively identifies plaques and detects circular boundaries in real-time. Collaborative efforts with domain experts enhance plaque burden interpretation through precise quantitative measurements. Future advancements may involve integrating advanced federated learning techniques and expanding datasets to further improve performance and applicability. This adaptable technology holds promise for environments handling sensitive, distributed data, offering potential to optimize outcomes in medical imaging and intervention. | |
| 653 | |a Polar coordinate models | ||
| 653 | |a Data analysis | ||
| 653 | |a Atherosclerosis | ||
| 653 | |a Collaboration | ||
| 653 | |a Hospitals | ||
| 653 | |a Medical imaging | ||
| 653 | |a Privacy | ||
| 653 | |a Two dimensional analysis | ||
| 653 | |a Elastic analysis | ||
| 653 | |a Data integration | ||
| 653 | |a Real time | ||
| 653 | |a Federated learning | ||
| 653 | |a Time measurement | ||
| 700 | 1 | |a Chen, Kai | |
| 700 | 1 | |a Tsung-Yu, Peng | |
| 700 | 1 | |a Huang, Wei-Chieh | |
| 773 | 0 | |t arXiv.org |g (Dec 19, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3148681740/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.15307 |