Výsledky vyhledávání - "Lee, Byoung Dai"

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MEDS-Net: Self-Distilled Multi-Encoders Network with Bi-Direction Maximum Intensity projections for Lung Nodule Detection

Autor: Usman, Muhammad, Rehman, Azka, Shahid, Abdullah, Latif, Siddique, Byon, Shi Sub, Lee, Byoung Dai, Kim, Sung Hyun, Lee, Byung il, Shin, Yeong Gil

In this study, we propose a lung nodule detection scheme which fully incorporates the clinic workflow of radiologists. Particularly, we exploit Bi-Directional Maximum intensity projection (MIP) images of various thicknesses (i.e., 3, 5 and 10mm) alon

Externí odkaz: http://arxiv.org/abs/2211.00003

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Dual-Stage Deeply Supervised Attention-based Convolutional Neural Networks for Mandibular Canal Segmentation in CBCT Scans

Autor: Rehman, Azka, Usman, Muhammad, Jawaid, Rabeea, Saleem, Amal Muhammad, Byon, Shi Sub, Kim, Sung Hyun, Lee, Byoung Dai, Lee, Byung il, Shin, Yeong Gil

Accurate segmentation of mandibular canals in lower jaws is important in dental implantology. Medical experts determine the implant position and dimensions manually from 3D CT images to avoid damaging the mandibular nerve inside the canal. In this pa

Externí odkaz: http://arxiv.org/abs/2210.03739

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Akademický článek

AI-Powered Synthesis of Structured Multimodal Breast Ultrasound Reports Integrating Radiologist Annotations and Deep Learning Analysis.

Autor: Azhar, Khadija¹ (AUTHOR) khadija.azhar@healthhub.kr, Lee, Byoung-Dai² (AUTHOR) blee@kgu.ac.kr, Byon, Shi Sub¹ (AUTHOR) terry.byon@healthhub.kr, Cho, Kyu Ran³ (AUTHOR) krcho@korea.ac.kr, Song, Sung Eun³ (AUTHOR) sungeuns@korea.ac.kr

Publikováno v: Bioengineering (Basel). Sep2024, Vol. 11 Issue 9, p890. 17p.

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Akademický článek

Automated assessment of pelvic radiographs using deep learning: A reliable diagnostic tool for pelvic malalignment

Autor: Moon, Ki-Ryum, Byon, Shi Sub, Kim, Sung Hyun, Lee, Byoung-Dai

Publikováno v: In Heliyon 30 April 2024 10(8)

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Akademický článek

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Volumetric Lung Nodule Segmentation using Adaptive ROI with Multi-View Residual Learning

Autor: Usman, Muhammad, Lee, Byoung-Dai, Byon, Shi Sub, Kim, Sung Hyun, Byung-ilLee

Accurate quantification of pulmonary nodules can greatly assist the early diagnosis of lung cancer, which can enhance patient survival possibilities. A number of nodule segmentation techniques have been proposed, however, all of the existing techniqu

Externí odkaz: http://arxiv.org/abs/1912.13335

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