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2020 – today
- 2024
[j5]Sion An
, Jaehong Kim, Soopil Kim, Philip Chikontwe, Jiwook Jung, Hyejeong Jeon, Sang Hyun Park:
Few-shot anomaly detection using positive unlabeled learning with cycle consistency and co-occurrence features. Expert Syst. Appl. 256: 124890 (2024)- [j4]Soopil Kim, Heejung Park, Myeongkyun Kang, Kyong Hwan Jin, Ehsan Adeli, Kilian M. Pohl, Sanghyun Park:
Federated learning with knowledge distillation for multi-organ segmentation with partially labeled datasets. Medical Image Anal. 95: 103156 (2024) - [j3]Myeongkyun Kang, Soopil Kim, Kyong Hwan Jin, Ehsan Adeli, Kilian M. Pohl, Sanghyun Park:
FedNN: Federated learning on concept drift data using weight and adaptive group normalizations. Pattern Recognit. 149: 110230 (2024) - [j2]Sion An, Soopil Kim, Philip Chikontwe, Sanghyun Park:
Dual Attention Relation Network With Fine-Tuning for Few-Shot EEG Motor Imagery Classification. IEEE Trans. Neural Networks Learn. Syst. 35(11): 15479-15493 (2024) - [c9]Soopil Kim, Sion An, Philip Chikontwe, Myeongkyun Kang, Ehsan Adeli, Kilian M. Pohl, Sanghyun Park:
Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection. AAAI 2024: 8591-8599 - [c8]Siwoo Nam, Hyun Namgung, Jaehoon Jeong, Miguel Luna, Soopil Kim, Philip Chikontwe, Sanghyun Park:
InstaSAM: Instance-Aware Segment Any Nuclei Model with Point Annotations. MICCAI (4) 2024: 232-242 - [c7]Sion An, Myeongkyun Kang, Soopil Kim, Philip Chikontwe, Li Shen, Sang Hyun Park:
Subject-Adaptive Transfer Learning Using Resting State EEG Signals for Cross-Subject EEG Motor Imagery Classification. MICCAI (11) 2024: 678-688 - [i7]Sion An, Myeongkyun Kang, Soopil Kim, Philip Chikontwe, Li Shen, Sang Hyun Park:
Subject-Adaptive Transfer Learning Using Resting State EEG Signals for Cross-Subject EEG Motor Imagery Classification. CoRR abs/2405.19346 (2024) - 2023
- [j1]Soopil Kim, Philip Chikontwe, Sion An, Sanghyun Park:
Uncertainty-aware semi-supervised few shot segmentation. Pattern Recognit. 137: 109292 (2023) - [c6]Myeongkyun Kang, Philip Chikontwe, Soopil Kim, Kyong Hwan Jin, Ehsan Adeli-Mosabbeb, Kilian M. Pohl, Sanghyun Park:
One-Shot Federated Learning on Medical Data Using Knowledge Distillation with Image Synthesis and Client Model Adaptation. MICCAI (2) 2023: 521-531 - [i6]Soopil Kim, Sion An, Philip Chikontwe, Myeongkyun Kang, Ehsan Adeli, Kilian M. Pohl, Sanghyun Park:
Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection. CoRR abs/2312.13783 (2023) - 2022
- [c5]Philip Chikontwe, Soopil Kim, Sang Hyun Park:
CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classification. CVPR 2022: 14534-14543 - [c4]Heejung Park, Gyeong Min Lee, Soopil Kim, Ga Hyung Ryu, Areum Jeong, Min Sagong, Sang Hyun Park:
A Meta-Learning Approach for Medical Image Registration. ISBI 2022: 1-5 - [i5]Philip Chikontwe, Soopil Kim, Sang Hyun Park:
CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classification. CoRR abs/2203.13465 (2022) - 2021
- [c3]Soopil Kim, Sion An, Philip Chikontwe, Sang Hyun Park:
Bidirectional RNN-based Few Shot Learning for 3D Medical Image Segmentation. AAAI 2021: 1808-1816 - [i4]Heejung Park, Gyeong Min Lee, Soopil Kim, Ga Hyung Ryu, Areum Jeong, Sang Hyun Park, Min Sagong:
A Meta-Learning Approach for Medical Image Registration. CoRR abs/2104.10447 (2021) - [i3]Soopil Kim, Philip Chikontwe, Sang Hyun Park:
Uncertainty-Aware Semi-Supervised Few Shot Segmentation. CoRR abs/2110.08954 (2021) - 2020
- [c2]Sion An, Soopil Kim, Philip Chikontwe, Sang Hyun Park:
Few-Shot Relation Learning with Attention for EEG-based Motor Imagery Classification. IROS 2020: 10933-10938 - [i2]Sion An, Soopil Kim, Philip Chikontwe, Sang Hyun Park:
Few-Shot Relation Learning with Attention for EEG-based Motor Imagery Classification. CoRR abs/2003.01300 (2020) - [i1]Soopil Kim, Sion An, Philip Chikontwe, Sang Hyun Park:
Bidirectional RNN-based Few Shot Learning for 3D Medical Image Segmentation. CoRR abs/2011.09608 (2020)
2010 – 2019
- 2019
- [c1]Soopil Kim, Miguel Luna, Philip Chikontwe, Sang Hyun Park:
Two-Step U-Nets for Brain Tumor Segmentation and Random Forest with Radiomics for Survival Time Prediction. BrainLes@MICCAI (1) 2019: 200-209
Coauthor Index
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