Paul H. Yi
Orcid: 0000-0001-9433-8093
According to our database1,
Paul H. Yi
authored at least 22 papers
between 2019 and 2024.
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Bibliography
2024
The clinician-AI interface: intended use and explainability in FDA-cleared AI devices for medical image interpretation.
npj Digit. Medicine, 2024
Improving Multi-Center Generalizability of GAN-Based Fat Suppression using Federated Learning.
CoRR, 2024
Anytime, Anywhere, Anyone: Investigating the Feasibility of Segment Anything Model for Crowd-Sourcing Medical Image Annotations.
CoRR, 2024
Out-of-Distribution Detection and Data Drift Monitoring using Statistical Process Control.
CoRR, 2024
Hidden in Plain Sight: Undetectable Adversarial Bias Attacks on Vulnerable Patient Populations.
CoRR, 2024
Expanding the Horizon: Enabling Hybrid Quantum Transfer Learning for Long-Tailed Chest X-Ray Classification.
Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2024
Privacy-Preserving Collaboration for Multi-Organ Segmentation via Federated Learning from Sites with Partial Labels.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024
2023
One Copy Is All You Need: Resource-Efficient Streaming of Medical Imaging Data at Scale.
CoRR, 2023
High-Throughput AI Inference for Medical Image Classification and Segmentation using Intelligent Streaming.
CoRR, 2023
Text2Cohort: Democratizing the NCI Imaging Data Commons with Natural Language Cohort Discovery.
CoRR, 2023
Optimizing Federated Learning for Medical Image Classification on Distributed Non-iid Datasets with Partial Labels.
CoRR, 2023
SegViz: A Federated Learning Framework for Medical Image Segmentation from Distributed Datasets with Different and Incomplete Annotations.
CoRR, 2023
Surgical Aggregation: A Federated Learning Framework for Harmonizing Distributed Datasets with Diverse Tasks.
CoRR, 2023
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
2022
Machine vs. Radiologist-Based Translations of RadLex: Implications for Multi-language Report Interoperability.
J. Digit. Imaging, 2022
Weakly Supervised Learning Significantly Reduces the Number of Labels Required for Intracranial Hemorrhage Detection on Head CT.
CoRR, 2022
From Competition to Collaboration: Making Toy Datasets on Kaggle Clinically Useful for Chest X-Ray Diagnosis Using Federated Learning.
CoRR, 2022
CoRR, 2022
2021
J. Digit. Imaging, 2021
2019
Deep-Learning-Based Semantic Labeling for 2D Mammography and Comparison of Complexity for Machine Learning Tasks.
J. Digit. Imaging, 2019
Deep Learning Method for Automated Classification of Anteroposterior and Posteroanterior Chest Radiographs.
J. Digit. Imaging, 2019