Výsledky vyhledávání - "Jang , Yeonggul"

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A Unified Approach for Comprehensive Analysis of Various Spectral and Tissue Doppler Echocardiography

Autor: Jeon, Jaeik, Kim, Jiyeon, Jang, Yeonggul, Yoon, Yeonyee E., Jeong, Dawun, Hong, Youngtaek, Lee, Seung-Ah, Chang, Hyuk-Jae

Doppler echocardiography offers critical insights into cardiac function and phases by quantifying blood flow velocities and evaluating myocardial motion. However, previous methods for automating Doppler analysis, ranging from initial signal processin

Externí odkaz: http://arxiv.org/abs/2311.08439

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Self supervised convolutional kernel based handcrafted feature harmonization: Enhanced left ventricle hypertension disease phenotyping on echocardiography

Autor: Lee, Jina, Hong, Youngtaek, Jeong, Dawun, Jang, Yeonggul, Jeon, Jaeik, Jeong, Sihyeon, Jung, Taekgeun, Yoon, Yeonyee E., Moon, Inki, Lee, Seung-Ah, Chang, Hyuk-Jae

Radiomics, a medical imaging technique, extracts quantitative handcrafted features from images to predict diseases. Harmonization in those features ensures consistent feature extraction across various imaging devices and protocols. Methods for harmon

Externí odkaz: http://arxiv.org/abs/2310.08897

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Improving Out-of-Distribution Detection in Echocardiographic View Classication through Enhancing Semantic Features

Autor: Jeon, Jaeik, Ha, Seongmin, Jang, Yeonggul, Yoon, Yeonyee E., Kim, Jiyeon, Jeong, Hyunseok, Jeong, Dawun, Hong, Youngtaek, Chang, Seung-Ah Lee Hyuk-Jae

In echocardiographic view classification, accurately detecting out-of-distribution (OOD) data is essential but challenging, especially given the subtle differences between in-distribution and OOD data. While conventional OOD detection methods, such a

Externí odkaz: http://arxiv.org/abs/2308.16483

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Bayesian approaches for Quantifying Clinicians' Variability in Medical Image Quantification

Autor: Jeon, Jaeik, Jang, Yeonggul, Hong, Youngtaek, Shim, Hackjoon, Kim, Sekeun

Medical imaging, including MRI, CT, and Ultrasound, plays a vital role in clinical decisions. Accurate segmentation is essential to measure the structure of interest from the image. However, manual segmentation is highly operator-dependent, which lea

Externí odkaz: http://arxiv.org/abs/2207.01868

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