Two-Step U-Nets for Brain Tumor Segmentation and Random Forest with Radiomics for Survival Time Prediction. - Soopil Kim; Miguel Luna; Philip Chikontwe; Sang Hyun Park 004 (BrainLes@MICCAI (1), 2019)

Few-Shot Relation Learning with Attention for EEG-based Motor Imagery Classification. - Sion An; Soopil Kim; Philip Chikontwe; Sang Hyun Park 004 (IROS, 2020)

Bidirectional RNN-based Few Shot Learning for 3D Medical Image Segmentation. - Soopil Kim; Sion An; Philip Chikontwe; Sang Hyun Park 004 (AAAI, 2021)

Uncertainty-Aware Semi-Supervised Few Shot Segmentation. - Soopil Kim; Philip Chikontwe; Sang Hyun Park 004 (CoRR, 2021)

CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classification. - Philip Chikontwe; Soopil Kim; Sang Hyun Park 004 (CVPR, 2022)

A Meta-Learning Approach for Medical Image Registration. - Heejung Park; Gyeong Min Lee; Soopil Kim; Ga Hyung Ryu; Areum Jeong; Min Sagong; Sang Hyun Park 004 (ISBI, 2022)

One-Shot Federated Learning on Medical Data Using Knowledge Distillation with Image Synthesis and Client Model Adaptation. - Myeongkyun Kang; Philip Chikontwe; Soopil Kim; Kyong Hwan Jin; Ehsan Adeli-Mosabbeb; Kilian M. Pohl; Sanghyun Park 004 (MICCAI (2), 2023)

Uncertainty-aware semi-supervised few shot segmentation. - Soopil Kim; Philip Chikontwe; Sion An; Sanghyun Park 004 (Pattern Recognit., 2023)

Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection. - Soopil Kim; Sion An; Philip Chikontwe; Myeongkyun Kang; Ehsan Adeli 001; Kilian M. Pohl; Sanghyun Park 004 (AAAI, 2024)

Subject-Adaptive Transfer Learning Using Resting State EEG Signals for Cross-Subject EEG Motor Imagery Classification. - Sion An; Myeongkyun Kang; Soopil Kim; Philip Chikontwe; Li Shen; Sang Hyun Park 004 (MICCAI (11), 2024)

InstaSAM: Instance-Aware Segment Any Nuclei Model with Point Annotations. - Siwoo Nam; Hyun Namgung; Jaehoon Jeong; Miguel Luna; Soopil Kim; Philip Chikontwe; Sanghyun Park 004 (MICCAI (4), 2024)

Few-shot anomaly detection using positive unlabeled learning with cycle consistency and co-occurrence features. - Sion An; Jaehong Kim; Soopil Kim; Philip Chikontwe; Jiwook Jung; Hyejeong Jeon; Sang Hyun Park 004 (Expert Syst. Appl., 2024)

Federated learning with knowledge distillation for multi-organ segmentation with partially labeled datasets. - Soopil Kim; Heejung Park; Myeongkyun Kang; Kyong Hwan Jin; Ehsan Adeli 001; Kilian M. Pohl; Sanghyun Park 004 (Medical Image Anal., 2024)

FedNN: Federated learning on concept drift data using weight and adaptive group normalizations. - Myeongkyun Kang; Soopil Kim; Kyong Hwan Jin; Ehsan Adeli 001; Kilian M. Pohl; Sanghyun Park 004 (Pattern Recognit., 2024)

Dual Attention Relation Network With Fine-Tuning for Few-Shot EEG Motor Imagery Classification. - Sion An; Soopil Kim; Philip Chikontwe; Sanghyun Park 004 (IEEE Trans. Neural Networks Learn. Syst., 2024)

MC-NuSeg: Multi-Contour Aware Nuclei Instance Segmentation with Segment Anything Model. - Hyun Namgung; Siwoo Nam; Soopil Kim; Sang Hyun Park (IPMI (1), 2025)

Efficient one-shot federated learning on medical data using knowledge distillation with image synthesis and client model adaptation. - Myeongkyun Kang; Philip Chikontwe; Soopil Kim; Kyong Hwan Jin; Ehsan Adeli 001; Kilian M. Pohl; Sanghyun Park 004 (Medical Image Anal., 2025)

Communication Efficient Federated Learning for Multi-Organ Segmentation via Knowledge Distillation With Image Synthesis. - Soopil Kim; Heejung Park; Philip Chikontwe; Myeongkyun Kang; Kyong Hwan Jin; Ehsan Adeli 001; Kilian M. Pohl; Sang Hyun Park 004 (IEEE Trans. Medical Imaging, 2025)

Subject-adaptive meta-learning for personalized BCI: A fusion of resting-state EEG signal and task-specific information. - Sion An; Myeongkyun Kang; Soopil Kim; Philip Chikontwe; Li Shen; Sang Hyun Park 004 (Inf. Fusion, 2026)