FPUS23: An Ultrasound Fetus Phantom Dataset With Deep Neural Network Evaluations for Fetus Orientations, Fetal Planes, and Anatomical Features
Author:
Affiliation:
1. Institute of Computer Engineering, Technische Universität Wien (TU Wien),, Vienna, Austria
2. Philips Research, Eindhoven, AE, The Netherlands
3. Division of Engineering, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
Funder
Moore4Medical Project funded by the ECSEL Joint Undertaking
Doctoral College Resilient Embedded Systems, which is run jointly by the Faculty of Informatics, TU Wien, and UAS Technikum Wien
Open Access Funding Programme
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10146252.pdf?arnumber=10146252
Reference30 articles.
1. Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning
2. Automated Fetal Head Detection and Circumference Estimation from Free-Hand Ultrasound Sweeps Using Deep Learning in Resource-Limited Countries
3. Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes
4. Automated measurement of fetal head circumference using 2D ultrasound images
5. APQ: Joint Search for Network Architecture, Pruning and Quantization Policy
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