Unsupervised nonlinear dimensionality reduction machine learning methods applied to multiparametric MRI in cerebral ischemia: preliminary results. - Vishwa S. Parekh; Jeremy R. Jacobs; Michael A. Jacobs (Image Processing, 2014)

A multidimensional data visualization and clustering method: Consensus similarity mapping. - Vishwa S. Parekh; Michael A. Jacobs (ISBI, 2016)

Unsupervised Non Linear Dimensionality Reduction Machine Learning methods applied to Multiparametric MRI in cerebral ischemia: Preliminary Results. - Vishwa S. Parekh; Jeremy R. Jacobs; Michael A. Jacobs (CoRR, 2016)

Multiparametric Deep Learning Tissue Signatures for a Radiological Biomarker of Breast Cancer: Preliminary Results. - Vishwa S. Parekh; Katarzyna J. Macura; Susan C. Harvey; Ihab Kamel; Riham El Khouli; David A. Bluemke; Michael A. Jacobs (CoRR, 2018)

MPRAD: A Multiparametric Radiomics Framework. - Vishwa S. Parekh; Michael A. Jacobs (CoRR, 2018)

Advanced machine learning informatics modeling using clinical and radiological imaging metrics for characterizing breast tumor characteristics with the OncotypeDX gene array. - Michael A. Jacobs; Christopher Umbricht; Vishwa S. Parekh; Riham El Khouli; Leslie Cope; Katarzyna J. Macura; Susan C. Harvey; Antonio C. Wolff (CoRR, 2018)

Radiomic Synthesis Using Deep Convolutional Neural Networks. - Vishwa S. Parekh; Michael A. Jacobs (ISBI, 2019)

Tumor Connectomics: Mapping the intra-tumoral complex interaction network. - Vishwa S. Parekh; Michael A. Jacobs (CoRR, 2019)

Multiparametric Deep Learning and Radiomics for Tumor Grading and Treatment Response Assessment of Brain Cancer: Preliminary Results. - Vishwa S. Parekh; John Laterra; Chetan Bettegowda; Alex E. Bocchieri; Jay J. Pillai; Michael A. Jacobs (CoRR, 2019)

Multiparametric Deep Learning Tissue Signatures for Muscular Dystrophy: Preliminary Results. - Alex E. Bocchieri; Vishwa S. Parekh; Kathryn R. Wagner; Shivani Ahlawat; Vladimir Braverman; Doris G. Leung; Michael A. Jacobs (CoRR, 2019)

Multitask radiological modality invariant landmark localization using deep reinforcement learning. - Vishwa S. Parekh; Alex E. Bocchieri; Vladimir Braverman; Michael A. Jacobs (MIDL, 2020)

Cross-Domain Federated Learning in Medical Imaging. - Vishwa S. Parekh; Shuhao Lai; Vladimir Braverman; Jeff Leal; Steven Rowe; Jay J. Pillai; Michael A. Jacobs (CoRR, 2021)

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)

Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging. - Guangyao Zheng; Samson Zhou; Vladimir Braverman; Michael A. Jacobs; Vishwa Sanjay Parekh (MIDL, 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)

Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging. - Guangyao Zheng; Michael A. Jacobs; Vladimir Braverman; 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)

Multi-environment lifelong deep reinforcement learning for medical imaging. - Guangyao Zheng; Shuhao Lai; Vladimir Braverman; Michael A. Jacobs; Vishwa S. Parekh (CoRR, 2023)

A framework for dynamically training and adapting deep reinforcement learning models to different, low-compute, and continuously changing radiology deployment environments. - Guangyao Zheng; Shuhao Lai; Vladimir Braverman; Michael A. Jacobs; 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)

Towards a Collective Medical Imaging AI: Enabling Continual Learning from Peers. - Guangyao Zheng; Vladimir Braverman; Jeffrey Leal; Steven Rowe; Doris G. Leung; Michael A. Jacobs; 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)

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)

Demographic Predictability in 3D CT Foundation Embeddings. - Guangyao Zheng; Michael A. Jacobs; Vishwa S. Parekh (CoRR, 2024)

A collective AI via lifelong learning and sharing at the edge. - Andrea Soltoggio; Eseoghene Ben-Iwhiwhu; Vladimir Braverman; Eric Eaton; Benjamin Epstein; Yunhao Ge; Lucy Halperin; Jonathan P. How; Laurent Itti; Michael A. Jacobs; Pavan Kantharaju; Long Le; et al. (Nat. Mac. Intell., 2024)

Towards Fair Medical AI: Adversarial Debiasing of 3D CT Foundation Embeddings. - Guangyao Zheng; Michael A. Jacobs; Vladimir Braverman; Vishwa S. Parekh (CoRR, 2025)