Deep Learning Method for Automated Classification of Anteroposterior and Posteroanterior Chest Radiographs. - Tae Kyung Kim; Paul H. Yi; Jinchi Wei; Ji Won Shin; Gregory D. Hager; Ferdinand K. Hui; Haris I. Sair; Cheng Ting Lin (J. Digit. Imaging, 2019)

Deep-Learning-Based Semantic Labeling for 2D Mammography and Comparison of Complexity for Machine Learning Tasks. - Paul H. Yi; Abigail Lin; Jinchi Wei; Alice C. Yu; Haris I. Sair; Ferdinand K. Hui; Gregory D. Hager; Susan C. Harvey (J. Digit. Imaging, 2019)

DeepCAT: Deep Computer-Aided Triage of Screening Mammography. - Paul H. Yi; Dhananjay Singh 002; Susan C. Harvey; Gregory D. Hager; Lisa A. Mullen (J. Digit. Imaging, 2021)

Estimating and Controlling for Fairness via Sensitive Attribute Predictors. - Beepul Bharti; Paul H. Yi; Jeremias Sulam (CoRR, 2022)

From Competition to Collaboration: Making Toy Datasets on Kaggle Clinically Useful for Chest X-Ray Diagnosis Using Federated Learning. - Pranav Kulkarni; Adway U. Kanhere; Paul H. Yi; Vishwa S. Parekh (CoRR, 2022)

Weakly Supervised Learning Significantly Reduces the Number of Labels Required for Intracranial Hemorrhage Detection on Head CT. - Jacopo Teneggi; Paul H. Yi; Jeremias Sulam (CoRR, 2022)

Machine vs. Radiologist-Based Translations of RadLex: Implications for Multi-language Report Interoperability. - Christian J. Park; Paul H. Yi; Hussain Al Yousif; Kenneth C. Wang (J. Digit. Imaging, 2022)

Estimating and Controlling for Equalized Odds via Sensitive Attribute Predictors. - Beepul Bharti; Paul H. Yi; Jeremias Sulam (NeurIPS, 2023)

Surgical Aggregation: A Federated Learning Framework for Harmonizing Distributed Datasets with Diverse Tasks. - Pranav Kulkarni; Adway U. Kanhere; Paul H. Yi; Vishwa S. Parekh (CoRR, 2023)

SegViz: A Federated Learning Framework for Medical Image Segmentation from Distributed Datasets with Different and Incomplete Annotations. - Adway U. Kanhere; Pranav Kulkarni; Paul H. Yi; Vishwa S. Parekh (CoRR, 2023)

Optimizing Federated Learning for Medical Image Classification on Distributed Non-iid Datasets with Partial Labels. - Pranav Kulkarni; Adway U. Kanhere; Paul H. Yi; Vishwa S. Parekh (CoRR, 2023)

Text2Cohort: Democratizing the NCI Imaging Data Commons with Natural Language Cohort Discovery. - Pranav Kulkarni; Adway U. Kanhere; Paul H. Yi; Vishwa S. Parekh (CoRR, 2023)

High-Throughput AI Inference for Medical Image Classification and Segmentation using Intelligent Streaming. - Pranav Kulkarni; Sean Garin; Adway U. Kanhere; Eliot Siegel; Paul H. Yi; Vishwa S. Parekh (CoRR, 2023)

One Copy Is All You Need: Resource-Efficient Streaming of Medical Imaging Data at Scale. - Pranav Kulkarni; Adway U. Kanhere; Eliot Siegel; Paul H. Yi; Vishwa S. Parekh (CoRR, 2023)

Privacy-Preserving Collaboration for Multi-Organ Segmentation via Federated Learning from Sites with Partial Labels. - Adway U. Kanhere; Pranav Kulkarni; Paul H. Yi; Vishwa S. Parekh (CVPR Workshops, 2024)

Hidden in Plain Sight: Undetectable Adversarial Bias Attacks on Vulnerable Patient Populations. - Pranav Kulkarni; Andrew Chan; Nithya Navarathna; Skylar Chan; Paul H. Yi; Vishwa Sanjay Parekh (MIDL, 2024)

From Isolation to Collaboration: Federated Class-Heterogeneous Learning for Chest X-Ray Classification. - Pranav Kulkarni; Adway U. Kanhere; Paul H. Yi; Vishwa S. Parekh (ML4H@NeurIPS, 2024)

Expanding the Horizon: Enabling Hybrid Quantum Transfer Learning for Long-Tailed Chest X-Ray Classification. - Skylar Chan; Pranav Kulkarni; Paul H. Yi; Vishwa S. Parekh (QCE, 2024)

Out-of-Distribution Detection and Data Drift Monitoring using Statistical Process Control. - Ghada Zamzmi; Kesavan Venkatesh; Brandon Nelson; Smriti Prathapan; Paul H. Yi; Berkman Sahiner; Jana G. Delfino (CoRR, 2024)

Anytime, Anywhere, Anyone: Investigating the Feasibility of Segment Anything Model for Crowd-Sourcing Medical Image Annotations. - Pranav Kulkarni; Adway U. Kanhere; Dharmam Savani; Andrew Chan; Devina Chatterjee; Paul H. Yi; Vishwa S. Parekh (CoRR, 2024)

Improving Multi-Center Generalizability of GAN-Based Fat Suppression using Federated Learning. - Pranav Kulkarni; Adway U. Kanhere; Harshita Kukreja; Vivian Zhang; Paul H. Yi; Vishwa S. Parekh (CoRR, 2024)

The clinician-AI interface: intended use and explainability in FDA-cleared AI devices for medical image interpretation. - Stephanie L. McNamara; Paul H. Yi; William Lotter (npj Digit. Medicine, 2024)

Sufficient and Necessary Explanations (and What Lies in Between). - Beepul Bharti; Paul H. Yi; Jeremias Sulam (CPAL, 2025)

Towards Virtual Clinical Trials of Radiology AI with Conditional Generative Modeling. - Benjamin D. Killeen; Bohua Wan; Aditya V. Kulkarni; Nathan Drenkow; Michael Oberst; Paul H. Yi; Mathias Unberath (CoRR, 2025)

Multiaccuracy and Multicalibration via Proxy Groups. - Beepul Bharti; Mary Versa Clemens-Sewall; Paul H. Yi; Jeremias Sulam (CoRR, 2025)

Aligning Explanations with Human Communication. - Jacopo Teneggi; Zhenzhen Wang; Paul H. Yi; Tianmin Shu; Jeremias Sulam (CoRR, 2025)