*: Corresponding Author
Bold: Member of Augmented Intelligence Lab.
2024
- [C45] Mask-Free Neuron Concept Annotation for Interpreting Neural Networks in Medical Domain
Hyeon Bae Kim, Yong Hyun Ahn (co-first), Seong Tae Kim*
International Conference On Medical Image Computing & Computer Assisted Intervention (MICCAI), 2024 (Marrakesh) - [C44] MonoWAD: Weather-Adaptive Diffusion Model for Robust Monocular 3D Object Detection
Youngmin Oh, Hyung-Il Kim, Seong Tae Kim*, Jung Uk Kim*
European Conference on Computer Vision (ECCV), 2024 (Milan) - [C43] WWW: A Unified Framework for Explaining What, Where and Why of Neural Networks by Interpretation of Neuron Concept
Yong Hyun Ahn, Hyeon Bae Kim, Seong Tae Kim*
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Seattle) - [C42] Do You Remember? Dense Video Captioning with Cross-Modal Memory Retrieval
Minkuk Kim, Hyeon Bae Kim, Jinyoung Moon, Jinwoo Choi*, Seong Tae Kim*
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Seattle) - [C41] Retrieval-Augmented Natural Language Reasoning for Explainable Visual Question Answering
Su Hyeon Lim, Minkuk Kim, Hyeon Bae Kim, Seong Tae Kim*
International Conference on Image Processing Workshop (ICIPW), 2024 (Abu Dhabi) - [C40] Resource-Efficient Medical Report Generation using Large Language Models
Abdullah, Ameer Hamza, Seong Tae Kim*
International Technical Conference on Circuits/Systems, Computers, and Communications, 2024 (Okinawa) - [C39] Dissecting Mixed-Sample Data Augmentation Models via Neural-Concept Association
Soyoun Won, Sung-Ho Bae, Seong Tae Kim*
International Conference on Information Networking, 2024 (Ho Chi Minh City) - [J20] OnDev-LCT: On-Device Lightweight Convolutional Transformers towards Federated Learning
Chu Myaet Thwal, Minh N. H. Nguyen, Ye Lin Tun, Seong Tae Kim, My T. Thai, Choong Seon Hong
Neural Networks, 2024
2023
- [J19] Exploiting Recollection Effects for Memory-based Video Object Segmentation
Enki Cho, Minkuk Kim, Hyung-Il Kim, Jinyoung Moon, Seong Tae Kim*
Image and Vision Computing, 2023 - [C38] LINe: Out-of-Distribution Detection by Leveraging Important Neurons
Yong Hyun Ahn, Gyeong-Moon Park*, Seong Tae Kim*
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Vancouver) - [C37] Towards Robust Audio-based Vehicle Detection via Importance-aware Audio-Visual Learning
Jung Uk Kim, Seong Tae Kim
International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023 (Rhodes) - [J18] Interactive Segmentation for COVID-19 Infection Quantification on Longitudinal CT scans
Michelle Xiao-Lin Foo, Seong Tae Kim*, Magdalini Paschali, Leili Goli, Egon Burian, Marcus Makowski, Rickmer Braren, Nassir Navab, Thomas Wendler
IEEE Access, 2023 - [J17] Time Series Anomaly Detection using Transformer-based GAN with Two-Step Masking
Ahyung Shin, Seong Tae Kim*, Gyeong-Moon Park*
IEEE Access, 2023 - [J16] A Hybrid Multimodal Emotion Recognition Framework for UX Evaluation Using Generalized Mixture Functions
Muhammad Asif Razzaq, Jamil Hussain, Jaehun Bang, Cam-Hao Hua, Fahad Ahmed Satti, Ubaid Ur Rehman, Hafiz Syed Muhammad Bilal, Seong Tae Kim*, Sungyoung Lee*
Sensors, 2023 - [J15] Improved abdominal multi-organ segmentation via 3D boundary-constrained deep neural networks
Samra Irshad, Douglas PS Gomes, Seong Tae Kim*
IEEE Access, 2023 - [J14] Towards Label-Efficient Neural Network Training: Diversity-based Sampling in Semi-Supervised Active Learning
Felix Buchert, Nassir Navab, Seong Tae Kim*
IEEE Access, 2023
2022
- [C36] Analyzing the Effects of Handling Data Imbalance on Learned Features by Looking Into the Models
Ashkan Khakzar, Yawei Li, Yang Zhang, Mirac Sanisoglu, Seong Tae Kim, Mina Rezaei, Bernd Bischl, Nassir Navab
ICML Interpretable Machine Learning in Healthcare Workshop (ICML Workshop), 2022 (Baltimore) - [T6] Longitudinal Self-Supervision for COVID-19 Pathology Quantification
Tobias Czempiel, Coco Rogers, Matthias Keicher, Magdalini Paschali, Rickmer Braren, Egon Burian, Marcus Makowski, Nassir Navab, Thomas Wendler, Seong Tae Kim*
arXiv:2203.10804, 2022 - [C35] Exploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning
Felix Buchert, Nassir Navab, Seong Tae Kim*
International Conference on Pattern Recognition (ICPR), 2022 (Montreal) - [J13] Robust Perturbation for Visual Explanation: Cross-checking Mask Optimization to Avoid Class Distortion
Junho Kim, Seongyeop Kim, Seong Tae Kim, Yong Man Ro
IEEE Transactions on Image Processing (TIP), 2022
2021
- [C34] Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information
Yang Zhang, Ashkan Khakzar, Yawei Li, Azade Farshad, Seong Tae Kim*, Nassir Navab
Conference on Neural Information Processing Systems (NeurIPS), 2021 (Virtual) - [C33] Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs
Seong Tae Kim*, Leili Goli (co-first), Magdalini Paschali, Ashkan Khakzar, Matthias Keicher, Tobias Czempiel, Egon Burian, Rickmer Braren, Nassir Navab, Thomas Wendler
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2021 (Virtual) - [C32] Towards Semantic Interpretation of Thoracic Disease and COVID-19 Diagnosis Models
Ashkan Khakzar, Sabrina Musatian, Jonas Buchberger, Icxel Valeriano Quiroz, Nikolaus Pinger, Soroosh Baselizadeh, Seong Tae Kim*, Nassir Navab
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2021 (Virtual) - [C31] Explaining COVID-19 and Thoracic Pathology Model Predictions by Identifying Informative Input Features
Ashkan Khakzar, Yang Zhang, Wejdene Mansour, Yuezhi Cai, Yawei Li, Yucheng Zhang, Seong Tae Kim*, Nassir Navab
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2021 (Virtual) - [C30] OperA: Attention-Regularized Transformers for Surgical Phase Recognition
Tobias Czempiel, Magdalini Paschali, Daniel Ostler, Seong Tae Kim, Benjamin Busam, Nassir Navab
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2021 (Virtual) - [C29] Neural Response Interpretation through the Lens of Critical Pathways
Ashkan Khakzar, Soroosh Baselizadeh, Saurabh Khanduja, Christian Rupprecht, Seong Tae Kim*, Nassir Navab
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Virtual) - [J12] Longitudinal Brain MR Image Modeling using Personalized Memory for Alzheimer’s Disease
Seong Tae Kim*, Umut Küçükaslan (co-first), Nassir Navab
IEEE Access, 2021 - [T5] GLOWin: A Flow-based Invertible Generative Framework for Learning Disentangled Feature Representations in Medical Images
Aadhithya Sankar, Matthias Keicher, Rami Eisawy, Abhijeet Parida, Franz Pfister, Seong Tae Kim*, Nassir Navab
arXiv:2103.10868, 2021 - [C28] Butterfly-Net: Spatial-Temporal Architecture for Medical Image Segmentation
Tetiana Klymenko, Seong Tae Kim, Kirsten Lauber, Christopher Kurz, Guillaume Landry, Nassir Navab, Shadi Albarqouni
IEEE International Symposium on Biomedical Imaging (ISBI), 2021 (Virtual) - [J11] CUA Loss: Class Uncertainty-Aware Gradient Modulation for Robust Object Detection
Jung Uk Kim, Seong Tae Kim, Hong Joo Lee, Sangmin Lee, Yong Man Ro
IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2021
Before 2021
- [J10] Force-Ultrasound Fusion: Bringing Spine Robotic-US to the Next “Level”
Maria Tirindelli, Maria Victorova, Javier Esteban, Seong Tae Kim, David Navarro-Alarcon, Yong Ping Zheng, Nassir Navab
IEEE Robotics and Automation Letters (RA-L), 2020
Presented at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020 (Virtual) - [J9] Multimodal Facial Biometrics Recognition: Dual-stream Convolutional Neural Networks with Multi-feature Fusion Layers
Leslie Ching Ow Tiong, Seong Tae Kim, Yong Man Ro
Image and Vision Computing (ImaVis), 2020 - [J8] Lightweight and effective facial landmark detection using adversarial learning with face geometric map generative network
Hong Joo Lee, Seong Tae Kim, Hakmin Lee, Yong Man Ro
IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2020 - [C27] Self-supervised out-of-distribution detection in brain CT scans
Abinav Ravi Venkatakrishnan, Seong Tae Kim*, Rami Eisawy, Franz Pfister, Nassir Navab
Medical Imaging Meets NeurIPS (NeurIPS Workshop), 2020 (Virtual) - [C26] TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks
Tobias Czempiel, Magdalini Paschali, Matthias Keicher, Walter Simson, Hubertus Feussner, Seong Tae Kim, Nassir Navab
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2020 (Virtual) - [C25] Spatio-temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation
Stefan Denner, Ashkan Khakzar, Moiz Sajid, Mahdi Saleh, Ziga Spiclin, Seong Tae Kim*, Nassir Navab
BrainLes at International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAIW), 2020 (Virtual) - [C24] Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack
Hakmin Lee, Hong Joo Lee, Seong Tae Kim, Yong Man Ro
_British Machine Vision Conference (BMVC) _, 2020 (Virtual) - [C23] Towards high-performance object detection: Task-specific design considering classification and localization separation
Jung Uk Kim, Seong Tae Kim, Eun Sung Kim, Sang-Keun Moon, Yong Man Ro
International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020 (Virtual Barcelona) - [T4] Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation
Hong Joo Lee, Seong Tae Kim(co-first), Hakmin Lee, Nassir Navab, Yong Man Ro
arXiv:2004.02200, 2020 - [T3] Confident Coreset for Active Learning in Medical Image Analysis
Seong Tae Kim, Farrukh Mushtaq, Nassir Navab
arXiv:2004.02200, 2020 - [J7] Attended relation feature representation of facial dynamics for facial authentication
Seong Tae Kim, Yong Man Ro
IEEE Transactions on Information Forensics and Security (TIFS), 2019 - [J6] Implementation of multimodal biometric recognition via multi-feature deep learning networks and feature fusion
Leslie Ching Ow Tiong, Seong Tae Kim, Yong Man Ro
Multimedia Tools and Applications (MTAP), 2018 - [C22] Building a breast-sentence dataset: Its usefulness for computer-aided diagnosis
Hyebin Lee, Seong Tae Kim, Yong Man Ro
Visual Recognition for Medical Image at International Conference on Computer Vision (ICCVW), 2019 (Seoul) - [C21] Realistic breast mass generation through BIRADS category
Hakmin Lee, Seong Tae Kim, Jae-Hyeok Lee, Yong Man Ro
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2019 (Shenzhen) - [C20] Generation of multimodal generation using visual word constraint model for explainable computer-aided diagnosis
Hyebin Lee, Seong Tae Kim, Yong Man Ro
Workshop on Interpretability of Machine Intelligence in Medical Image Computing at MICCAI (MICCAIW), 2019 (Shenzhen) - [C19] Probenet: Probing deep networks
Jae-Hyeok Lee, Seong Tae Kim, and Yong Man Ro
IEEE International Conference on Image Processing (ICIP), 2019 (Taiwan) - [C18] Visual evidence for interpreting diagnostic decision of deep neural network in computer-aided diagnosis (Oral)
Seong Tae Kim, Jae-Hyeok Lee, Yong Man Ro
SPIE Medical Imaging (SPIE MI), 2019 (San Diego) - [J5] Visually interpretable deep network for diagnosis of breast masses on mammograms
Seong Tae Kim, Jae-Hyeok Lee, Hakmin Lee, Yong Man Ro
Physics in Medicine and Biology (PMB), 2018 - [C17] Facial dynamics interpreter network: What are the important relations between local dynamics for facial trait estimation?
Seong Tae Kim,Yong Man Ro
European Conference on Computer Vision (ECCV), 2018 (Munich) - [C16] Feature2Mass: Visual feature processing in latent space for realistic labeled mass generation
Jae-Hyeok Lee, Seong Tae Kim, Hakmin Lee, Yong Man Ro
Bioimage Computing Workshop at European Conference on Computer Vision (ECCVW), 2018 (Munich) - [C15] ICADx: Interpretable computer aided diagnosis of breast masses (Best Student Paper Award, Oral)
Seong Tae Kim, Hakmin Lee, Hak Gu Kim, Yong Man Ro
SPIE Medical Imaging (SPIE MI), 2018 (Houston) - [C14] Convolution with logarithmic filter groups for efficient shallow CNN
Tae Kwan Lee, Wissam J Baddar, Seong Tae Kim, Yong Man Ro
International Conference on Multimedia Modeling (MMM), 2018 (Bangkok) - [C13] Teacher and student joint learning for compact facial landmark detection network
Hongju Lee, Wissam Baddar, Hak Gu Kim, Seong Tae Kim, Yong Man Ro
International Conference on Multimedia Modeling (MMM), 2018 (Bangkok) - [T2] Differential generative adversarial networks: Synthesizing non-linear facial variations with limited number of training data
Geonmo Gu, Seong Tae Kim, Kihyun Kim, Wissam J Baddar, Yong Man Ro
arXiv:1711.10267, 2017 - [T1] EvaluationNet: Can human skill be evaluated by deep networks?
Seong Tae Kim, Yong Man Ro
arXiv:1705.11077, 2017 - [J4] Latent feature representation with depth directional long-term recurrent learning for breast masses in digital breast tomosynthesis
Dae Hoe Kim, Seong Tae Kim, Jung Min Chang (Seoul National University Hospital), Yong Man Ro
Physics in Medicine and Biology (PMB), 2017 - [C12] Multi-scale facial scanning via spatial LSTM for latent facial feature representation
Seong Tae Kim, Yeoreum Choi, and Yong Man Ro
International Conference of the Biometrics Special Interest Group (BIOSIG), 2017 (Darmstadt) - [C11] Adaptive attention fusion network for visual question answering
Geonmo Gu, Seong Tae Kim, and Yong Man Ro
IEEE International Conference on Multimedia and Expo (ICME), 2017 (Hong Kong) - [C10] Facial dynamic modelling using long short-term memory network: Analysis and application to face authentication
Seong Tae Kim, Dae Hoe Kim, Yong Man Ro
IEEE International Conference on Biometrics: Thoery, Applications, and Systems (BTAS), 2016 (Buffalo) - [C9] Spatio-temporal representation for face authentication by using multi-task learning with human attributes
Seong Tae Kim, Dae Hoe Kim, Yong Man Ro
IEEE International Conference on Image Processing (ICIP), 2016 (Phoenix) - [C8] A deep facial landmarks detection with facial contour and facial components constraint
Wissam Baddar, Jisoo Son, Dae Hoe Kim, Seong Tae Kim, Yong Man Ro
IEEE International Conference on Image Processing (ICIP), 2016 (Phoenix) - [C7] Latent feature representation with 3-D Multi-view convolutional neural network for bilateral analysis in digital breast tomosynthesis
Dae Hoe Kim, Seong Tae Kim, Yong Man Ro
IEEE International Conference on Acoustics, speech and signal processing (ICASSP), 2016 (Shanghai) - [J3] Detection of masses in digital breast tomosynthesis using complementary information of simulated projection
Seong Tae Kim, Dae Hoe Kim, Yong Man Ro
Medical Physics (MP), 2015 - [J2] Improving mass detection using combined feature representations from projection views and reconstructed volume of DBT and boosting based classification with feature selection
Dae Hoe Kim, Seong Tae Kim, and Yong Man Ro
Physics in Medicine and Biology (PMB), 2015 - [C6] Region matching based on local structure information in ipsilateral digital breast tomosynthesis views
Seong Tae Kim, Dae Hoe Kim, Dong Jin Ji, Yong Man Ro
IEEE International Conference on Image Processing (ICIP), 2015 (Québec City) - [C5] Feature extraction from bilateral dissimilarity in digital breast tomosynthesis reconstructed volume (Top-10% Paper)
Dae Hoe Kim, Seong Tae Kim, Wissam J Baddar, Yong Man Ro
IEEE International Conference on Image Processing (ICIP), 2015 (Québec City) - [C4] Combination of conspicuity improved synthetic mammograms and digital breast tomosynthesis: A promising approach for mass detection (Oral)
Seong Tae Kim, Dae Hoe Kim, Yong Man Ro
SPIE Medical Imaging (MI), 2015 (Orlando) - [C3] Feature extraction from inter-view similarity of DBT projection views (Honorable Mention Poster Award)
Dae Hoe Kim, Seong Tae Kim, Yong Man Ro
SPIE Medical Imaging (MI), 2015 (Orlando) - [J1] Breast mass detection using slice conspicuity in 3D reconstructed digital breast volumes
Seong Tae Kim, Dae Hoe Kim, Yong Man Ro
Physics in Medicine and Biology (PMB), 2014 - [C2] Generation of conspicuity-improved synthetic image from digital breast tomosynthesis (Oral)
Seong Tae Kim, Dae Hoe Kim, Yong Man Ro
International Conference on Digital Signal Processing, 2014 (Hong Kong) - [C1] Mass detection based on pooled mass probability map of 3D reconstructed slices in digital breast tomosynthesis (Oral)
Seong Tae Kim, Dae Hoe Kim, Eun Suk Cha, Yong Man Ro
IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 2014 (Valencia)
Patents (Registered)
- [P14] Interactive computer-aided diagnosis method for lesion diagnosis and the system thereof (KR 10-2281988)
- [P13] Interpreting method for diagnostic decision of deep network using breast imaging-reporting and data system and the system thereof (KR 10-2223255)
- [P12] Method for interpreting visual evidence according to breast mass characteristics and the system thereof (KR 10-2216279)
- [P11] Automated Facial Expression Recognizing Systems on N frames, Methods, and Computer-Readable Mediums thereof (KR 10-2152120)
- [P10] Machine learning system using joint learning and the method thereof (KR 10-2100973)
- [P9] Medical image apparatus and control method for the same (KR 10-2096410)
- [P8] Method for generating breast masses according to the lesion characteristics and the system thereof (KR 10-2067340)
- [P7] Analysis method of relations of face movements and the system thereof (KR 10-2054058)
- [P6] Method and system for artificial intelligence based video surveillance using deep learning (KR 10-1995107)
- [P5] Explainable computer-aided diagnosis and the method thereof (KR 10-1938992)
- [P4] Apparatus and method for generating reprojection images for diagnostic feature extraction (US 10,092,263)
- [P3] System for instructional video learning and evaluation using deep learning (KR 10-1893290)
- [P2] Method and system for automatic biometric authentication based on facial spatio-temporal features (KR 10-1802061)
- [P1] Lesion classification apparatus, and method of modifying lesion classification data (US 9,547,896)