Research

*: Corresponding Author
Bold: Member of Augmented Intelligence Lab.

2024

• [C41] 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)
• [C40] 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)
• [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)