Publications

2025

  1. Review Article
    Pitfalls and Best Practices in Evaluation of Algorithmic Biases in Radiology
    Paul H. Yi, Preetham Bachina, Beepul Bharti, Sean P. Garin, Adway Kanhere, Pranav Kulkarni, David Li, Vishwa S. Parekh, Samantha M. Santomartino, Linda Moy, and Jeremias Sulam
    Radiology, 315(2), e241674. May 2025
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  2. Towards Resource-Efficient Streaming of Large-Scale Medical Image Datasets for Deep Learning
    Pranav Kulkarni, Adway Kanhere, Eliot L. Siegel, Paul H. Yi, and Vishwa S. Parekh
    In Medical Imaging with Deep Learning (MIDL). Jul 2025
    HTML PDF Code arXiv
  3. Federated Class-Heterogeneous Radiology Report Labeling with Surgical Aggregation
    Nikhil Shah, Pranav Kulkarni, Florence X. Doo, Ang Li, Michael A. Jacobs, and Vishwa S. Parekh
    In Medical Imaging with Deep Learning (MIDL). Jul 2025
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  4. Tiny Paper
    Negotiative Alignment: An interactive approach to human-AI co-adaptation
    Florence X. Doo, Nikhil Shah, Pranav Kulkarni, Vishwa S. Parekh, and Heng Huang
    In ICLR Workshop on Bidirectional Human-AI Alignment. Apr 2025
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  5. Book Chapter
    Expanding the Federated Horizon: Cross-Domain Techniques for Collective Intelligence
    Vishwa S. Parekh, Pranav Kulkarni, Adway Kanhere, and Michael A. Jacobs
    In Federated Learning for Medical Imaging, 57–68. Mar 2025
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2024

  1. Radiomics-Based Prediction of Demographics on Chest Radiographs: Looking Beyond Deep Learning for Risk of Bias
    Hadiseh Kavandi, Pranav Kulkarni, Sean P. Garin, Preetham Bachina, Vishwa S. Parekh, and Paul H. Yi
    American Journal of Roentgenology, 224(2), e2431963. Oct 2024
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  2. From Isolation to Collaboration: Federated Class-Heterogeneous Learning for Chest X-Ray Classification
    Pranav Kulkarni, Adway Kanhere, Paul H. Yi, and Vishwa S. Parekh
    In Machine Learning for Health Symposium (ML4H), 623–635. Dec 2024
    HTML PDF Code arXiv
  3. Dual Energy CT for Deep Learning-Based Segmentation and Volumetric Estimation of Early Ischemic Infarcts
    Peter Kamel, Mazhar Kahlid, Rachel Steger, Adway Kanhere, Pranav Kulkarni, Vishwa S. Parekh, Paul H. Yi, Uttam Bodanapally, and Dheeraj Gandhi
    Journal of Imaging Informatics in Medicine, 38(3), 1484–1495. Oct 2024
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  4. Children Are Not Small Adults: Addressing Limited Generalizability of an Adult Deep Learning Organ Segmentation Model to the Pediatric Population
    Devina Chatterjee, Adway Kanhere, Florence X. Doo, Jerry Zhao, Andrew Chan, Alexander Welsh, Pranav Kulkarni, Annie Trang, Vishwa S. Parekh, and Paul H. Yi
    Journal of Imaging Informatics in Medicine, 38(3), 1628–1641. Sep 2024
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  5. Optimizing Acute Stroke Segmentation on MRI using Deep Learning: Self-configuring Neural Networks Provide High Performance using only DWI Sequences
    Peter Kamel, Adway Kanhere, Pranav Kulkarni, Mazhar Kahlid, Rachel Steger, Uttam Bodanapally, Dheeraj Gandhi, Vishwa S. Parekh, and Paul H. Yi
    Journal of Imaging Informatics in Medicine, 38(2), 717–736. Aug 2024
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  6. Oral
    Expanding the Horizon: Enabling Hybrid Quantum Transfer Learning for Long-Tailed Chest X-Ray Classification
    Skylar Chan, Pranav Kulkarni, Paul H. Yi, and Vishwa S. Parekh
    In IEEE International Conference on Quantum Computing and Engineering (QCE), 572–582. Sep 2024
    HTML Code arXiv
  7. Oral
    Hidden in Plain Sight: Undetectable Adversarial Bias Attacks on Vulnerable Patient Populations
    Pranav Kulkarni, Andrew Chan, Nithya Navarathna, Skylar Chan, Paul H. Yi, and Vishwa S. Parekh
    In Medical Imaging with Deep Learning (MIDL), 793–821. Jul 2024
    HTML PDF Code arXiv
  8. Short Paper
    Anytime, Anywhere, Anyone: Investigating the Feasibility of Segment Anything Model for Crowd-Sourcing Medical Image Annotations
    Pranav Kulkarni, Adway Kanhere, Dharmam Savani, Andrew Chan, Devina Chatterjee, Paul H. Yi, and Vishwa S. Parekh
    Medical Imaging with Deep Learning (MIDL). Jul 2024
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  9. Short Paper
    Improving Multi-Center Generalizability of GAN-Based Fat Suppression using Federated Learning
    Pranav Kulkarni, Adway Kanhere, Harshita Kukreja, Vivian Zhang, Paul H. Yi, and Vishwa S. Parekh
    Medical Imaging with Deep Learning (MIDL). Jul 2024
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  10. ISLE: An Intelligent Streaming Framework for High-Throughput AI Inference in Medical Imaging
    Pranav Kulkarni, Adway Kanhere, Eliot L. Siegel, Paul H. Yi, and Vishwa S. Parekh
    Journal of Imaging Informatics in Medicine, 37(6), 3250–3263. Jun 2024
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  11. Best Poster Award
    Privacy-Preserving Collaboration for Multi-Organ Segmentation via Federated Learning from Sites with Partial Labels
    Adway Kanhere, Pranav Kulkarni, Paul H. Yi, and Vishwa S. Parekh
    In CVPR Workshop on Data Curation and Augmentation in Medical Imaging (CVPR DCA-in-MI), 2380–2387. Jun 2024
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  12. Economic and environmental costs of cloud for medical imaging and radiology artificial intelligence
    Florence X. Doo, Pranav Kulkarni, Eliot Siegel, Michael Toland, Paul H. Yi, Ruth C. Carlos, and Vishwa S. Parekh
    Journal of the American College of Radiology, 21(2), 248–256. Feb 2024
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2023

  1. Preprint
    Text2Cohort: Facilitating Intuitive Access to Biomedical Data with Natural Language Cohort Discovery
    Pranav Kulkarni, Adway Kanhere, Paul H. Yi, and Vishwa S. Parekh
    arXiv preprint arXiv:2305.07637. Dec 2023
    Code arXiv
  2. Coarse Race and Ethnicity Labels Mask Granular Underdiagnosis Disparities in Deep Learning Models for Chest Radiograph Diagnosis
    Preetham Bachina, Sean P. Garin, Pranav Kulkarni, Adway Kanhere, Jeremias Sulam, Vishwa S. Parekh, and Paul H. Yi
    Radiology, 309(2), e231693. Nov 2023
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  3. Preprint
    Optimizing Federated Learning for Medical Image Classification on Distributed Non-iid Datasets with Partial Labels
    Pranav Kulkarni, Adway Kanhere, Paul H. Yi, and Vishwa S. Parekh
    arXiv preprint arXiv:2303.06180. Mar 2023
    arXiv

2022

  1. Short Paper
    From Competition to Collaboration: Making Toy Datasets on Kaggle Clinically Useful for Chest X-Ray Diagnosis Using Federated Learning
    Pranav Kulkarni, Adway Kanhere, Paul H. Yi, and Vishwa S. Parekh
    Medical Imaging Meets NeurIPS Workshop. Nov 2022
    PDF arXiv