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Ji-Tae Shin

Sungkyunkwan University · Computer Science

About the Lab

Professor Ji-Tae Shin's research lab specializes in quality-of-service (QoS) optimization for next-generation communication networks, with a focus on intelligent video streaming, differentiated services (DiffServ), and adaptive networking under dynamic and resource-constrained environments. The lab also conducts cutting-edge research in medical image analysis, particularly in deep learning-based segmentation of early-stage pathological features in retinal and musculoskeletal imaging, such as drusen in age-related macular degeneration and cartilage in osteoarthritis. Additionally, the lab explores robust computer vision and deep learning techniques for autonomous driving systems, emphasizing resilience to environmental perturbations and real-time performance. The integration of AI-driven signal processing with QoS-aware networking and biomedical imaging forms the core of the lab’s interdisciplinary research direction.

QoS mappingmedical image segmentationdeep learningwireless sensor networksautonomous driving

Research Overview

Papers
236
Total Citations
2,098
Papers (5y)
51
Primary Field
Computer Science

Research Output Trend

Figures are computed from collected data and may differ slightly.

Publications per year (5y)
51total
2022
2023
2024
2025
2026
Citations per year (5y)
209total
20222023202420252026

Selected Papers

15
1
Article|145 citations·2008
Dynamic duty cycle and adaptive contention window based QoS-MAC protocol for wireless multimedia sensor networks
Navrati Saxena, Abhishek Roy, Jitae Shin
SJR Q1Computer Networks
Computer Networks and CommunicationsComputer Science
2
Article|127 citations·2001
Quality-of-service mapping mechanism for packet video in differentiated services network
Jitae Shin, Jong Won Kim, C.‐C. Jay Kuo
SJR Q1IEEE Transactions on Multimedia

This paper presents a futuristic framework for quality-of-service (QoS) mapping between practically categorized packet video and relative differentiated service (DiffServ or DS) network employing unified priority index and adaptive packet forwarding mechanism under a given pricing model (e.g., DiffServ level differentiated price/packet). Video categorization is based on the relative priority index (RPI), which represents the relative preference per each packet in terms of loss and delay. We prop

Computer Networks and CommunicationsComputer Science
3
Article|83 citations·2019
Incorporated region detection and classification using deep convolutional networks for bone age assessment
Toan Duc Bui, Jae-Joon Lee, Jitae Shin
SJR Q1Artificial Intelligence in Medicine
ArcheologyArts and Humanities
4
Article|58 citations·2019
Skip-connected 3D DenseNet for volumetric infant brain MRI segmentation
Toan Duc Bui, Jitae Shin, Taesup Moon
SJR Q1Biomedical Signal Processing and Control
Computer Vision and Pattern RecognitionComputer Science
5
Article|39 citations·2022
Generating future fundus images for early age-related macular degeneration based on generative adversarial networks
Quang Trung Pham, Sang-Il Ahn, Jitae Shin, Su Jeong Song
SJR Q1Computer Methods and Programs in Biomedicine
Radiology, Nuclear Medicine and ImagingMedicine
6
Article|32 citations·2020
Automatic Drusen Segmentation for Age-Related Macular Degeneration in Fundus Images Using Deep Learning
Quang Trung Pham, Sang-Il Ahn, Su Jeong Song, Jitae Shin
SJR Q2ElectronicsOA

Drusen are the main aspect of detecting age-related macular degeneration (AMD). Ophthalmologists can evaluate the condition of AMD based on drusen in fundus images. However, in the early stage of AMD, the drusen areas are usually small and vague. This leads to challenges in the drusen segmentation task. Moreover, due to the high-resolution fundus images, it is hard to accurately predict the drusen areas with deep learning models. In this paper, we propose a multi-scale deep learning model for dr

Radiology, Nuclear Medicine and ImagingMedicine
7
Article|22 citations·2021
Robust object detection under harsh autonomous‐driving environments
Youngjun Kim, Hyekyoung Hwang, Jitae Shin
SJR Q2IET Image ProcessingOA

Abstract In the autonomous driving environment, object instances in an image can be affected by various factors such as camera, driving state, weather, and system component. However, the deep learning‐based vision systems are vulnerable to perturbation, which contains noise. Thus, robust object detection under harsh autonomous‐driving environments is a more difficult than the generic situation. In this paper, it is found that not only the accuracy, but also the speed of the non‐maximum suppressi

Artificial IntelligenceComputer Science
8
Article|21 citations·2016
Fully automated, level set-based segmentation for knee MRIs using an adaptive force function and template: data from the osteoarthritis initiative
Chunsoo Ahn, Toan Duc Bui, Yong-woo Lee, Jitae Shin, Hyunjin Park
SJR Q2BioMedical Engineering OnLineOA

BACKGROUND: This study focuses on osteoarthritis (OA), which affects millions of adults and occurs in knee cartilage. Diagnosis of OA requires accurate segmentation of cartilage structures. Existing approaches to cartilage segmentation of knee imaging suffer from either lack of fully automatic algorithm, sub-par segmentation accuracy, or failure to consider all three cartilage tissues. METHODS: We propose a novel segmentation algorithm for knee cartilages with level set-based segmentation method

RheumatologyMedicine
9
Article|20 citations·2001
Dynamic QoS mapping control for streaming video in relative service differentiation networks
Jitae Shin, Jin‐Gyeong Kim, JongWon Kim, C.‐C. Jay Kuo
European Transactions on Telecommunications

Abstract A dynamic quality of service (QoS) mapping control scheme, which includes feedforward and feedback QoS control, is proposed for the differentiated services (DiffServ) networks in this work. To achieve reliable and consistent end—to—end video streaming with relative service differentiation, the proposed solution consists of two parts: (1) relative priority—based indexing and categorization of streaming video content at the sending end—system and (2) dynamic and aggregate QoS mapping cont

Computer Networks and CommunicationsComputer Science
10
Article|20 citations·2007
QuESt: a QoS‐based energy efficient sensor routing protocol
Navrati Saxena, Abhishek Roy, Jitae Shin
Wireless Communications and Mobile Computing

Abstract Rapid penetration of smart wireless devices and enormous growth of wireless communication technologies has already set the stage for deployment of wireless sensor networks (WSNs). While these small sensor nodes are often considered as the future of wireless communications, they also suffer from energy constraints. On the other hand, with increasing demand for real‐time services in next generation wireless networks, quality‐of‐service (QoS)‐based routing has emerged as an interesting res

Computer Networks and CommunicationsComputer Science
11
Article|19 citations·2007
Cross-layer error-control with low-overhead ARQ for H.264 video transmission over wireless LANs
Jeong-Yong Choi, Jitae Shin
SJR Q1Computer Communications
Signal ProcessingComputer Science
12
Article|16 citations·2021
Construction of a Soundscape-Based Media Art Exhibition to Improve User Appreciation Experience by Using Deep Neural Networks
Youngjun Kim, Hayoung Jeong, Jun‐Dong Cho, Jitae Shin
SJR Q2ElectronicsOA

The objective of this study was to improve user experience when appreciating visual artworks with soundscape music chosen by a deep neural network based on weakly supervised learning. We also propose a multi-faceted approach to measuring ambiguous concepts, such as the subjective fitness, implicit senses, immersion, and availability. We showed improvements in appreciation experience, such as the metaphorical and psychological transferability, time distortion, and cognitive absorption, with in-de

Speech and HearingHealth Professions
13
book|15 citations·2003
Quality of service for internet multimedia
Jitae Shin, Daniel Lee, C.‐C. Jay Kuo
CERN Document Server (European Organization for Nuclear Research)

Continuous media applications have exceptionally stringent QoS requirements, and QoS for multimedia will remain a challenge well into the future. The solution begins with service-differentiated networks capable of providing appropriate grades of service to each application. This book takes the next step, showing how continuous media applications and QoS-enabled networks can interact, and offering a leading-edge framework in which applications and the network can cooperatively optimize end-to-end

Computer Vision and Pattern RecognitionComputer Science
14
Article|14 citations·2002
Relative priority based QoS interaction between video applications and differentiated service networks
Jitae Shin, JongWon Kim, C.‐C. Jay Kuo

In this research, we investigate quality of service (QoS) interaction provisioning between video applications and components of the DiffServ network. QoS interaction is performed through the mapping of video packets based on the relative quality index (RQI), which represents the relative preference in terms of loss and delay, onto the adaptive packet forwarding mechanism in a DiffServ network. To verify the efficiency of the proposed strategy, the end-to-end performance is evaluated through erro

Computer Networks and CommunicationsComputer Science
15
Article|13 citations·2021
Future Image Synthesis for Diabetic Retinopathy Based on the Lesion Occurrence Probability
Sang-Il Ahn, Quang Trung Pham, Jitae Shin, Su Jeong Song
SJR Q2ElectronicsOA

Diabetic Retinopathy (DR) is one of the major causes of blindness. If the lesions observed in DR occur in the central part of the fundus, it can cause severe vision loss, and we call this symptom Diabetic Macular Edema (DME). All patients with DR potentially have DME since DME can occur in every stage of DR. While synthesizing future fundus images, the task of predicting the progression of the disease state is very challenging since we need a lot of longitudinal data over a long period of time.

Radiology, Nuclear Medicine and ImagingMedicine

Research Areas

Computer Networks and CommunicationsComputer Vision and Pattern RecognitionElectrical and Electronic EngineeringArtificial IntelligenceSignal ProcessingRadiology, Nuclear Medicine and Imaging

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