Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/45874| Title: | Comparison of Radio Frequency vs beamformed ultrasound data for infarct classification with CNNs | Authors: | Tostes, Paulo Balinisteanu, Anca E. MOURA FERREIRA, Sara Williams, Helena Voigt, Jens-Uwe D'hooge, Jan |
Issue Date: | 2024 | Publisher: | IEEE | Source: | 2024 IEEE Ultrasonic, ferroelectrics, and frequency control joint symposium, UFFC-JS 2024, IEEE, (Art N° 8318) | Series/Report: | IEEE International Symposium on Applications of Ferroelectrics | Abstract: | Ultrasound is the most practical, cost-effective and patient-friendly clinical imaging modality. However, Computed Tomography and Magnetic Resonance Imaging outperform echography when texture or tissue characterization is clinically required. As AI-methodologies could help resolve this, a Deep Learning algorithm was applied to classify localized infarcted tissue from static ultrasound recordings. | Notes: | Tostes, P (corresponding author), Katholieke Univ Leuven, Dept Cardiovasc Sci, Leuven, Belgium. paulo.tostes@kuleuven.be; anca.balinisteanu@ymail.com; sara.mouraferreira@gmail.com; helena.williams@kuleuven.be; jens-uwe.voigt@uzleuven.be; jan.dhooge@kuleuven.be |
Keywords: | Convolutional Neural Networks;Deep Learning;Infarct Classification;Radio Frequency;Ultrasound Texture | Document URI: | http://hdl.handle.net/1942/45874 | ISBN: | 979-8-3503-7191-8; 979-8-3503-7190-1 | DOI: | 10.1109/UFFC-JS60046.2024.10794162 | ISI #: | 001428150100622 | Rights: | IEEE | Category: | C1 | Type: | Proceedings Paper |
| Appears in Collections: | Research publications |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Comparison of Radio Frequency vs beamformed ultrasound data for infarct classification with CNNs.pdf Restricted Access | Published version | 286.17 kB | Adobe PDF | View/Open Request a copy |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.