Seong Tae Kim
Seong Tae Kim
Assistant Professor of Computer Science, Kyung Hee University
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Zitiert von
Zitiert von
TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks
T Czempiel, M Paschali, M Keicher, W Simson, H Feussner, ST Kim, ...
MICCAI, 343-352, 2020
One small step for generative ai, one giant leap for agi: A complete survey on chatgpt in aigc era
C Zhang, C Zhang, C Li, Y Qiao, S Zheng, SK Dam, M Zhang, JU Kim, ...
arXiv preprint arXiv:2304.06488, 2023
OperA: Attention-Regularized Transformers for Surgical Phase Recognition
T Czempiel, M Paschali, D Ostler, ST Kim, B Busam, N Navab
MICCAI, 604–614, 2021
Latent feature representation with 3-D multi-view deep convolutional neural network for bilateral analysis in digital breast tomosynthesis
DH Kim, ST Kim, YM Ro
ICASSP, 927-931, 2016
Generation of multimodal justification using visual word constraint model for explainable computer-aided diagnosis
H Lee, ST Kim, YM Ro
MICCAI Workshop, 21-29, 2019
Implementation of multimodal biometric recognition via multi-feature deep learning networks and feature fusion
LCO Tiong, ST Kim, YM Ro
Multimedia Tools and Applications 78, 22743-22772, 2019
Multimodal facial biometrics recognition: Dual-stream convolutional neural networks with multi-feature fusion layers
LCO Tiong, ST Kim, YM Ro
Image and Vision Computing, 103977, 2020
Neural response interpretation through the lens of critical pathways
A Khakzar, S Baselizadeh, S Khanduja, C Rupprecht, ST Kim, N Navab
CVPR, 13528-13538, 2021
Force-ultrasound fusion: Bringing spine robotic-us to the next “level”
M Tirindelli, M Victorova, J Esteban, ST Kim, D Navarro-Alarcon, ...
IEEE Robotics and Automation Letters 5 (4), 5661-5668, 2020
ICADx: interpretable computer aided diagnosis of breast masses
ST Kim, H Lee, HG Kim, YM Ro
Medical Imaging 2018: Computer-Aided Diagnosis 10575, 450-459, 2018
Fine-grained neural network explanation by identifying input features with predictive information
Y Zhang, A Khakzar, Y Li, A Farshad, ST Kim, N Navab
NeurIPS 34, 20040-20051, 2021
Visually interpretable deep network for diagnosis of breast masses on mammograms
ST Kim, JH Lee, H Lee, YM Ro
Physics in Medicine & Biology 63 (23), 235025, 2018
Self-Supervised Out-of-Distribution Detection in Brain CT Scans
AR Venkatakrishnan, ST Kim, R Eisawy, F Pfister, N Navab
NeurIPS Workshop, 2020
Latent feature representation with depth directional long-term recurrent learning for breast masses in digital breast tomosynthesis
DH Kim, ST Kim, JM Chang, YM Ro
Physics in Medicine & Biology 62 (3), 1009, 2017
A deep facial landmarks detection with facial contour and facial components constraint
WJ Baddar, J Son, DH Kim, ST Kim, YM Ro
2016 IEEE International Conference on Image Processing (ICIP), 3209-3213, 2016
LINe: Out-of-Distribution Detection by Leveraging Important Neurons
YH Ahn, GM Park, ST Kim
CVPR, 19852-19862, 2023
Spatio-temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation
S Denner, A Khakzar, M Sajid, M Saleh, Z Spiclin, ST Kim, N Navab
MICCAI Workshop, 2020
Lightweight and effective facial landmark detection using adversarial learning with face geometric map generative network
HJ Lee, ST Kim, H Lee, YM Ro
IEEE Transactions on Circuits and Systems for Video Technology 30 (3), 771-780, 2019
Attended Relation Feature Representation of Facial Dynamics for Facial Authentication
ST Kim, YM Ro
IEEE Transactions on Information Forensics and Security 14 (7), 1768-1778, 2019
Convolution with Logarithmic Filter Groups for Efficient Shallow CNN
TK Lee, WJ Baddar, ST Kim, YM Ro
International Conference on Multimedia Modeling, 117-129, 2018
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