7 citations · 15 across the 18 of their papers we have counts for
18 papers
WSPolypNet: Weakly Supervised Polyp Localization in Colonoscopy Videos
Giseong Hwang, Minjae Jo, Yeonghyeon Park +9
Because dense frame-level annotation of colonoscopy videos is costly, we propose WSPolypNet, a weakly supervised framework for polyp localization using only video-level labels. WSP…
Dosimetric Impact of Hidden Input Parameters in Inverse Optimization Algorithms for GYN HDR Brachytherapy
YeongHyeon Park, Shiqin Su, Sarath Vijayan +3
Inverse optimization (IO) algorithms are used in GYN HDR brachytherapy planning, with user parameter settings embedded in commercial TPS. To examine the dosimetric influence of hid…
ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting
Myung Jin Kim, YeongHyeon Park, Il Dong Yun
This paper proposes a simple yet effective convolutional module for long-term time series forecasting. The proposed block, inspired by the Auto-Regressive Integrated Moving Average…
Contrastive Language Prompting to Ease False Positives in Medical Anomaly Detection
YeongHyeon Park, Myung Jin Kim, Hyeong Seok Kim
A pre-trained visual-language model, contrastive language-image pre-training (CLIP), successfully accomplishes various downstream tasks with text prompts, such as finding images or…
Excision And Recovery: Visual Defect Obfuscation Based Self-Supervised Anomaly Detection Strategy
YeongHyeon Park, Sungho Kang, Myung Jin Kim +3
Due to scarcity of anomaly situations in the early manufacturing stage, an unsupervised anomaly detection (UAD) approach is widely adopted which only uses normal samples for traini…
Neural Network Training Strategy to Enhance Anomaly Detection Performance: A Perspective on Reconstruction Loss Amplification
YeongHyeon Park, Sungho Kang, Myung Jin Kim +4
Unsupervised anomaly detection (UAD) is a widely adopted approach in industry due to rare anomaly occurrences and data imbalance. A desirable characteristic of an UAD model is cont…
Edge Storage Management Recipe with Zero-Shot Data Compression for Road Anomaly Detection
YeongHyeon Park, Uju Gim, Myung Jin Kim
Recent studies show edge computing-based road anomaly detection systems which may also conduct data collection simultaneously. However, the edge computers will have small data stor…
Frequency of Interest-based Noise Attenuation Method to Improve Anomaly Detection Performance
YeongHyeon Park, Myung Jin Kim, Won Seok Park
Accurately extracting driving events is the way to maximize computational efficiency and anomaly detection performance in the tire frictional nose-based anomaly detection task. Thi…
Latent Vector Expansion using Autoencoder for Anomaly Detection
UJu Gim, YeongHyeon Park
Deep learning methods can classify various unstructured data such as images, language, and voice as input data. As the task of classifying anomalies becomes more important in the r…
Concise Logarithmic Loss Function for Robust Training of Anomaly Detection Model
YeongHyeon Park
Recently, deep learning-based algorithms are widely adopted due to the advantage of being able to establish anomaly detection models without or with minimal domain knowledge of the…
Noise Reduction and Driving Event Extraction Method for Performance Improvement on Driving Noise-based Surface Anomaly Detection
YeongHyeon Park, JoonSung Lee, Myung Jin Kim +1
Foreign substances on the road surface, such as rainwater or black ice, reduce the friction between the tire and the surface. The above situation will reduce the braking performanc…
Efficient Non-Compression Auto-Encoder for Driving Noise-based Road Surface Anomaly Detection
YeongHyeon Park, JongHee Jung
Wet weather makes water film over the road and that film causes lower friction between tire and road surface. When a vehicle passes the low-friction road, the accident can occur up…
Anomaly Detection Based on Multiple-Hypothesis Autoencoder
JoonSung Lee, YeongHyeon Park
Recently Autoencoder(AE) based models are widely used in the field of anomaly detection. A model trained with normal data generates a larger restoration error for abnormal data. Wh…
Self-Weighted Ensemble Method to Adjust the Influence of Individual Models based on Reliability
YeongHyeon Park, JoonSung Lee, Wonseok Park
Image classification technology and performance based on Deep Learning have already achieved high standards. Nevertheless, many efforts have conducted to improve the stability of c…
Non-Compression Auto-Encoder for Detecting Road Surface Abnormality via Vehicle Driving Noise
YeongHyeon Park, JongHee Jung
Road accident can be triggered by wet road because it decreases skid resistance. To prevent the road accident, detecting road surface abnomality is highly useful. In this paper, we…
The CNN-based Coronary Occlusion Site Localization with Effective Preprocessing Method
YeongHyeon Park, Il Dong Yun, Si-Hyuck Kang
The Coronary Artery Occlusion (CAO) acutely comes to human, and it highly threats the human's life. When CAO detected, Percutaneous Coronary Intervention (PCI) should be conducted…
Anomaly Detection in Particulate Matter Sensor using Hypothesis Pruning Generative Adversarial Network
YeongHyeon Park, Won Seok Park, Yeong Beom Kim
World Health Organization (WHO) provides the guideline for managing the Particulate Matter (PM) level because when the PM level is higher, it threats the human health. For managing…
Comparison of RNN Encoder-Decoder Models for Anomaly Detection
YeongHyeon Park, Il Dong Yun
In this paper, we compare different types of Recurrent Neural Network (RNN) Encoder-Decoders in anomaly detection viewpoint. We focused on finding the model that can learn the same…