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8篇 您的检索式：作者名="Hung Edward"

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		题名	作者	年代	出处	被引量
	1	Human NOTES Cholecystectomy: Transgastric Hybrid Technique显示文摘	Edward D. Auyang Eric S. Hungness Khashayar Vaziri John A. Martin Nathaniel J. Soper	2009	Journal of Gastrointestinal Surgery2009，，6：	2
	2	Optimization in Data Cube System Design显示文摘	Hung Edward Cheung David W Kao Ben	2004	Journal of Intelligent Information Systems2004，23，1：	1
	3	Analog VLSI implementations of auditory wavelet transforms using switched-capacitor circuits 显示文摘	Jyhfong Lin Wing - Hung Ki Edwards T Shamma S	1994	IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications1994，，9：	1
	4	Bioactive Stratified Polymer Ceramic-Hydrogel Scaffold for Integrative Osteochondral Repair显示文摘	Jie Jiang Amy Tang Gerard A. Ateshian X. Edward Guo Clark T. Hung Helen H. Lu	2010	Annals of Biomedical Engineering2010，，6：	1
	5	Human NOTES Cholecystectomy: Transgastric Hybrid Technique显示文摘	Edward D. Auyang Eric S. Hungness Khashayar Vaziri John A. Martin Nathaniel J. Soper	2009	Journal of Gastrointestinal Surgery2009，，6：	1
	6	High‐Mobility Field‐Effect Transistors Fabricated with Macroscopic Aligned Semiconducting Polymers显示文摘	Hsin‐Rong Tseng Hung Phan Chan Luo Ming Wang Louis A. Perez Shrayesh N. Patel Lei Ying Edward J. Kramer Thuc‐Quyen Nguyen Guillermo C. Bazan Alan J. Heeger	2014	Adv Mater2014，，19：	1
	7	Effect of identification and intervention age on language development for mandarin-speaking deaf children with high family involvement显示文摘	Hung-Ching Lin Cheng-Chien Yang Ya-Wen Chiang Pei-Wen Hung Edward Y. Yang Lillian Wang Grace Lin	2010	International Journal of Pediatric Otorhinolaryngology2010，，3：	1
	8	Weakly-and Semisupervised Probabilistic Segmentation and Quantification of Reverberation Artifacts显示文摘Objective and Impact Statement.We propose a weakly-and semisupervised,probabilistic needle-and-reverberation-artifact segmentation algorithm to separate the desired tissue-based pixel values from the superimposed artifacts.Our method models the intensity decay of artifact intensities and is designed to minimize the human labeling error.Introduction.Ultrasound image quality has continually been improving.However,when needles or other metallic objects are operating inside the tissue,the resulting reverberation artifacts can severely corrupt the surrounding image quality.Such effects are challenging for existing computer vision algorithms for medical image analysis.Needle reverberation artifacts can be hard to identify at times and affect various pixel values to different degrees.The boundaries of such artifacts are ambiguous,leading to disagreement among human experts labeling the artifacts.Methods.Our learning-based framework consists of three parts.The first part is a probabilistic segmentation network to generate the soft labels based on the human labels.These soft labels are input into the second part which is the transform function,where the training labels for the third part are generated.The third part outputs the final masks which quantifies the reverberation artifacts.Results.We demonstrate the applicability of the approach and compare it against other segmentation algorithms.Our method is capable of both differentiating between the reverberations from artifact-free patches and modeling the intensity fall-off in the artifacts.Conclusion.Our method matches state-of-the-art artifact segmentation performance and sets a new standard in estimating the per-pixel contributions of artifact vs underlying anatomy,especially in the immediately adjacent regions between reverberation lines.Our algorithm is also able to improve the performance of downstream image analysis algorithms.	Alex Ling Yu Hung Edward Chen John Galeotti	2022	Biomedical Engineering Frontiers2022，3，1：	0

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