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Minwoo Cho

Seoul National University · 医学

研究室紹介

Professor Minwoo Cho's research lab specializes in medical image analysis and artificial intelligence applications in healthcare, with a focus on enhancing diagnostic accuracy and clinical decision-making through advanced computational techniques. The lab develops AI-driven systems for medical imaging, including computer-aided detection in colonoscopy, super-resolution MRI reconstruction for neurodegenerative disease prediction, and generative models for data augmentation in radiology. Their work also extends to rehabilitation robotics, integrating user-intent detection and motion analysis to improve therapy outcomes in stroke patients.

medical image analysisartificial intelligence in healthcarecomputer-aided detectiongenerative models for medical imagingrehabilitation robotics

Research Overview

Papers
38
Total Citations
407
Papers (5y)
26
Primary Field
医学

Research Output Trend

Figures are computed from collected data and may differ slightly.

Publications per year (5y)
26total
2022
2023
2024
2025
2026
Citations per year (5y)
322total
20222023202420252026

Selected Papers

15
1
Article|136 citations·2022
Review of Smart Hospital Services in Real Healthcare Environments
Hyuktae Kwon, Sunhee An, Ho‐Young Lee, Won Chul, Sungwan Kim, Minwoo Cho, Hyoun‐Joong Kong
SJR Q2Healthcare Informatics ResearchOA

OBJECTIVE: Smart hospitals involve the application of recent information and communications technology (ICT) innovations to medical services; however, the concept of a smart hospital has not been rigorously defined. In this study, we aimed to derive the definition and service types of smart hospitals and investigate cases of each type. METHODS: A literature review was conducted regarding the background and technical characteristics of smart hospitals. On this basis, we conducted a focus group in

Biomedical EngineeringEngineering
2
Article|42 citations·2022
Colonoscopic image synthesis with generative adversarial network for enhanced detection of sessile serrated lesions using convolutional neural network
Dan Yoon, Hyoun‐Joong Kong, Byeong Soo Kim, Woo Sang Cho, Jung Chan Lee, Minwoo Cho, Min Hyuk Lim, Sun Young Yang, Seon Hee Lim, Jooyoung Lee, Ji Hyun Song, Goh Eun Chung
SJR Q1Scientific ReportsOA

Computer-aided detection (CADe) systems have been actively researched for polyp detection in colonoscopy. To be an effective system, it is important to detect additional polyps that may be easily missed by endoscopists. Sessile serrated lesions (SSLs) are a precursor to colorectal cancer with a relatively higher miss rate, owing to their flat and subtle morphology. Colonoscopy CADe systems could help endoscopists; however, the current systems exhibit a very low performance for detecting SSLs. We

OncologyMedicine
3
Article|17 citations·2024
Latent diffusion model-based MRI superresolution enhances mild cognitive impairment prognostication and Alzheimer's disease classification
Dan Yoon, Youho Myong, Young Gyun Kim, Yongsik Sim, Minwoo Cho, Byung‐Mo Oh, Sungwan Kim
SJR Q1NeuroImageOA

The diffusion model-based MRI SR enhances the resolution of brain MR images, significantly improving diagnostic and prognostic accuracy for AD and MCI. Superresolved 3T* images closely matched actual 3T MRIs in quality and volumetric accuracy, and notably improved the prediction performance of conversion from MCI to AD.

Radiology, Nuclear Medicine and ImagingMedicine
4
Article|16 citations·2019
Identification of cecum time-location in a colonoscopy video by deep learning analysis of colonoscope movement
Minwoo Cho, Jee Hyun Kim, Kyoung Sup Hong, Joo Sung Kim, Hyoun‐Joong Kong, Sungwan Kim
SJR Q1PeerJOA

BACKGROUND: Cecal intubation time is an important component for quality colonoscopy. Cecum is the turning point that determines the insertion and withdrawal phase of the colonoscope. For this reason, obtaining information related with location of the cecum in the endoscopic procedure is very useful. Also, it is necessary to detect the direction of colonoscope's movement and time-location of the cecum. METHODS: In order to analysis the direction of scope's movement, the Horn-Schunck algorithm was

OncologyMedicine
5
Article|16 citations·2019
Vision-Assisted Interactive Human-in-the-Loop Distal Upper Limb Rehabilitation Robot and its Clinical Usability Test
Hyung Seok Nam, Nhayoung Hong, Minwoo Cho, Chiwon Lee, Han Gil Seo, Sungwan Kim
SJR Q2Applied SciencesOA

In the context of stroke rehabilitation, simple structures and user-intent driven actuation are relevant features to facilitate neuroplasticity as well as deliver a sufficient number of repetitions during a single therapy session. A novel robotic treatment device for distal upper limb rehabilitation in stroke patients was developed, and a usability test was performed to assess its clinical feasibility. The rehabilitation robot was designed as a two-axis exoskeleton actuated by electric motors, c

RehabilitationMedicine
6
Article|16 citations·2023
Evaluating diagnostic content of AI-generated chest radiography: A multi-center visual Turing test
Youho Myong, Dan Yoon, Byeong Soo Kim, Young Gyun Kim, Yongsik Sim, Suji Lee, Jiyoung Yoon, Minwoo Cho, Sungwan Kim
SJR Q1PLoS ONEOA

BACKGROUND: Accurate interpretation of chest radiographs requires years of medical training, and many countries face a shortage of medical professionals to meet such requirements. Recent advancements in artificial intelligence (AI) have aided diagnoses; however, their performance is often limited due to data imbalance. The aim of this study was to augment imbalanced medical data using generative adversarial networks (GANs) and evaluate the clinical quality of the generated images via a multi-cen

Radiology, Nuclear Medicine and ImagingMedicine
7
Article|15 citations·2023
Deep Learning of Speech Data for Early Detection of Alzheimer’s Disease in the Elderly
Kichan Ahn, Minwoo Cho, Sukwha Kim, Kyu Eun Lee, Yoojin Song, Seok Yoo, So Yeon Jeon, Jeong Lan Kim, Dae Hyun Yoon, Hyoun‐Joong Kong
SJR Q2BioengineeringOA

BACKGROUND: Alzheimer's disease (AD) is the most common form of dementia, which makes the lives of patients and their families difficult for various reasons. Therefore, early detection of AD is crucial to alleviating the symptoms through medication and treatment. OBJECTIVE: Given that AD strongly induces language disorders, this study aims to detect AD rapidly by analyzing the language characteristics. MATERIALS AND METHODS: The mini-mental state examination for dementia screening (MMSE-DS), whi

Psychiatry and Mental healthMedicine
8
Article|14 citations·2019
Virtual Reality-based Control of Robotic Endoscope in Laparoscopic Surgery
Yeeun Jo, Yoon Jae Kim, Minwoo Cho, Chiwon Lee, Myungjoon Kim, Hye-Min Moon, Sungwan Kim
SJR Q2International Journal of Control Automation and Systems
SurgeryMedicine
9
Article|14 citations·2024
Enhanced multi-class pathology lesion detection in gastric neoplasms using deep learning-based approach and validation
Byeong Soo Kim, Bokyung Kim, Minwoo Cho, Hyunsoo Chung, Ji Kon Ryu, Sungwan Kim
SJR Q1Scientific ReportsOA

This study developed a new convolutional neural network model to detect and classify gastric lesions as malignant, premalignant, and benign. We used 10,181 white-light endoscopy images from 2606 patients in an 8:1:1 ratio. Lesions were categorized as early gastric cancer (EGC), advanced gastric cancer (AGC), gastric dysplasia, benign gastric ulcer (BGU), benign polyp, and benign erosion. We assessed the lesion detection and classification model using six-class, cancer versus non-cancer, and neop

Radiology, Nuclear Medicine and ImagingMedicine
10
Article|13 citations·2023
Mask R-CNN based multiclass segmentation model for endotracheal intubation using video laryngoscope
Seung Jae Choi, Dae Kon Kim, Byeong Soo Kim, Minwoo Cho, Joo Seong Jeong, You Hwan Jo, Kyoung Jun Song, Yu Jin Kim, Sungwan Kim
SJR Q2Digital HealthOA

Objective: Endotracheal intubation (ETI) is critical to secure the airway in emergent situations. Although artificial intelligence algorithms are frequently used to analyze medical images, their application to evaluating intraoral structures based on images captured during emergent ETI remains limited. The aim of this study is to develop an artificial intelligence model for segmenting structures in the oral cavity using video laryngoscope (VL) images. Methods: From 54 VL videos, clinicians manua

Anesthesiology and Pain MedicineMedicine
11
Article|12 citations·2024
Density clustering-based automatic anatomical section recognition in colonoscopy video using deep learning
Byeong Soo Kim, Minwoo Cho, Goh Eun Chung, Jooyoung Lee, Hae Yeon Kang, Dan Yoon, Woo Sang Cho, Jung Chan Lee, Jung Ho Bae, Hyoun‐Joong Kong, Sungwan Kim
SJR Q1Scientific ReportsOA

Recognizing anatomical sections during colonoscopy is crucial for diagnosing colonic diseases and generating accurate reports. While recent studies have endeavored to identify anatomical regions of the colon using deep learning, the deformable anatomical characteristics of the colon pose challenges for establishing a reliable localization system. This study presents a system utilizing 100 colonoscopy videos, combining density clustering and deep learning. Cascaded CNN models are employed to esti

OncologyMedicine
12
Article|11 citations·2015
Comparison of Efficacy Between Novel Robot-Assisted Laser Hair Removal and Physician-Directed Hair Removal
Hyoung-woo Lim, Dong Hun Lee, Minwoo Cho, Sungwoo Park, Wooseok Koh, Youdan Kim, Jin Ho Chung, Sungwan Kim
Photomedicine and Laser Surgery

OBJECTIVE: This study aimed to evaluate the number of laser irradiation sessions, process duration, and hair removal rate required for robot-assisted automatic versus physician-directed laser hair removal. BACKGROUND DATA: This research group previously developed and tested an automatic laser hair removal (LHR) system to provide uniform laser treatment distribution. METHODS: Six subjects 20-40 years of age, with skin types III-IV completed this study. A home-use LHR device with an 810 nm diode l

DermatologyMedicine
13
Article|10 citations·2018
A novel summary report of colonoscopy: timeline visualization providing meaningful colonoscopy video information
Minwoo Cho, Jee Hyun Kim, Hyoun‐Joong Kong, Kyoung Sup Hong, Sungwan Kim
SJR Q2International Journal of Colorectal Disease
OncologyMedicine
14
Article|8 citations·2024
A multimodal virtual vision platform as a next-generation vision system for a surgical robot
Young Gyun Kim, Jong Hyeon Lee, Jae Woo Shim, Wounsuk Rhee, Byeong Soo Kim, Dan Yoon, Min Jung Kim, Ji Won Park, Chang Wook Jeong, Han‐Kwang Yang, Minwoo Cho, Sungwan Kim
SJR Q2Medical & Biological Engineering & ComputingOA

Robot-assisted surgery platforms are utilized globally thanks to their stereoscopic vision systems and enhanced functional assistance. However, the necessity of ergonomic improvement for their use by surgeons has been increased. In surgical robots, issues with chronic fatigue exist owing to the fixed posture of the conventional stereo viewer (SV) vision system. A head-mounted display was adopted to alleviate the inconvenience, and a virtual vision platform (VVP) is proposed in this study. The VV

Computer Vision and Pattern RecognitionComputer Science
15
Article|7 citations·2023
A portable articulated dynamometer for ankle dorsiflexion and plantar flexion strength measurement: a design, validation, and user experience study
Seung Yeon Cho, Youho Myong, Sung-Woo Park, Minwoo Cho, Sungwan Kim
SJR Q1Scientific ReportsOA

Monitoring ankle strength is crucial for assessing daily activities, functional ability, and preventing lower extremity injuries. However, the current methods for measuring ankle strength are often unreliable or not easily portable to be used in clinical settings. Therefore, this study proposes a portable dynamometer with high reliability capable of measuring ankle dorsiflexion and plantar flexion. The proposed portable dynamometer comprised plates made of aluminum alloy 6061 and a miniature ten

Orthopedics and Sports MedicineMedicine

Research Areas

OncologySurgeryRadiology, Nuclear Medicine and ImagingDermatologyComputer Vision and Pattern RecognitionBiomedical Engineering

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