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Vishwa S. Parekh
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2020 – today
- 2024
- [j1]Andrea Soltoggio
, Eseoghene Ben-Iwhiwhu
A collective AI via lifelong learning and sharing at the edge. Nat. Mac. Intell. 6(3): 251-264 (2024) - [c6]Adway U. Kanhere, Pranav Kulkarni, Paul H. Yi, Vishwa S. Parekh:
Privacy-Preserving Collaboration for Multi-Organ Segmentation via Federated Learning from Sites with Partial Labels. CVPR Workshops 2024: 2380-2387 - [i24]Pranav Kulkarni, Andrew Chan, Nithya Navarathna, Skylar Chan, Paul H. Yi, Vishwa S. Parekh:
Hidden in Plain Sight: Undetectable Adversarial Bias Attacks on Vulnerable Patient Populations. CoRR abs/2402.05713 (2024) - [i23]Pranav Kulkarni, Adway U. Kanhere, Dharmam Savani, Andrew Chan, Devina Chatterjee, Paul H. Yi, Vishwa S. Parekh:
Anytime, Anywhere, Anyone: Investigating the Feasibility of Segment Anything Model for Crowd-Sourcing Medical Image Annotations. CoRR abs/2403.15218 (2024) - [i22]Pranav Kulkarni, Adway U. Kanhere, Harshita Kukreja, Vivian Zhang, Paul H. Yi, Vishwa S. Parekh:
Improving Multi-Center Generalizability of GAN-Based Fat Suppression using Federated Learning. CoRR abs/2404.07374 (2024) - [i21]Skylar Chan
Expanding the Horizon: Enabling Hybrid Quantum Transfer Learning for Long-Tailed Chest X-Ray Classification. CoRR abs/2405.00156 (2024) - 2023
- [c5]Guangyao Zheng, Samson Zhou, Vladimir Braverman, Michael A. Jacobs, Vishwa Sanjay Parekh:
Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging. MIDL 2023: 1751-1764 - [i20]Pranav Kulkarni
Surgical Aggregation: A Federated Learning Framework for Harmonizing Distributed Datasets with Diverse Tasks. CoRR abs/2301.06683 (2023) - [i19]Adway U. Kanhere, Pranav Kulkarni, Paul H. Yi, Vishwa S. Parekh:
SegViz: A Federated Learning Framework for Medical Image Segmentation from Distributed Datasets with Different and Incomplete Annotations. CoRR abs/2301.07074 (2023) - [i18]Guangyao Zheng, Samson Zhou, Vladimir Braverman, Michael A. Jacobs, Vishwa S. Parekh:
Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging. CoRR abs/2302.11510 (2023) - [i17]Pranav Kulkarni
Optimizing Federated Learning for Medical Image Classification on Distributed Non-iid Datasets with Partial Labels. CoRR abs/2303.06180 (2023) - [i16]Guangyao Zheng, Michael A. Jacobs, Vladimir Braverman, Vishwa S. Parekh:
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging. CoRR abs/2303.06783 (2023) - [i15]Pranav Kulkarni, Adway U. Kanhere, Paul H. Yi, Vishwa S. Parekh:
Text2Cohort: Democratizing the NCI Imaging Data Commons with Natural Language Cohort Discovery. CoRR abs/2305.07637 (2023) - [i14]Pranav Kulkarni, Sean Garin, Adway U. Kanhere, Eliot Siegel, Paul H. Yi, Vishwa S. Parekh:
High-Throughput AI Inference for Medical Image Classification and Segmentation using Intelligent Streaming. CoRR abs/2305.15617 (2023) - [i13]Guangyao Zheng, Shuhao Lai, Vladimir Braverman, Michael A. Jacobs, Vishwa S. Parekh:
Multi-environment lifelong deep reinforcement learning for medical imaging. CoRR abs/2306.00188 (2023) - [i12]Guangyao Zheng, Shuhao Lai, Vladimir Braverman, Michael A. Jacobs, Vishwa S. Parekh:
A framework for dynamically training and adapting deep reinforcement learning models to different, low-compute, and continuously changing radiology deployment environments. CoRR abs/2306.05310 (2023) - [i11]Pranav Kulkarni, Adway U. Kanhere, Eliot Siegel, Paul H. Yi, Vishwa S. Parekh:
One Copy Is All You Need: Resource-Efficient Streaming of Medical Imaging Data at Scale. CoRR abs/2307.00438 (2023) - 2022
- [i10]Pranav Kulkarni
From Competition to Collaboration: Making Toy Datasets on Kaggle Clinically Useful for Chest X-Ray Diagnosis Using Federated Learning. CoRR abs/2211.06212 (2022) - 2021
- [i9]Vishwa S. Parekh, Shuhao Lai, Vladimir Braverman, Jeff Leal, Steven Rowe, Jay J. Pillai, Michael A. Jacobs:
Cross-Domain Federated Learning in Medical Imaging. CoRR abs/2112.10001 (2021) - 2020
- [c4]Vishwa S. Parekh, Alex E. Bocchieri, Vladimir Braverman, Michael A. Jacobs:
Multitask radiological modality invariant landmark localization using deep reinforcement learning. MIDL 2020: 588-600
2010 – 2019
- 2019
- [c3]Vishwa S. Parekh, Michael A. Jacobs
Radiomic Synthesis Using Deep Convolutional Neural Networks. ISBI 2019: 1114-1117 - [i8]Vishwa S. Parekh, Michael A. Jacobs:
Tumor Connectomics: Mapping the intra-tumoral complex interaction network. CoRR abs/1901.09861 (2019) - [i7]Vishwa S. Parekh, John Laterra, Chetan Bettegowda, Alex E. Bocchieri, Jay J. Pillai
Multiparametric Deep Learning and Radiomics for Tumor Grading and Treatment Response Assessment of Brain Cancer: Preliminary Results. CoRR abs/1906.04049 (2019) - [i6]Alex E. Bocchieri, Vishwa S. Parekh, Kathryn R. Wagner, Shivani Ahlawat, Vladimir Braverman, Doris G. Leung, Michael A. Jacobs:
Multiparametric Deep Learning Tissue Signatures for Muscular Dystrophy: Preliminary Results. CoRR abs/1908.00175 (2019) - 2018
- [i5]Vishwa S. Parekh, Katarzyna J. Macura, Susan C. Harvey, Ihab Kamel, Riham El Khouli, David A. Bluemke, Michael A. Jacobs:
Multiparametric Deep Learning Tissue Signatures for a Radiological Biomarker of Breast Cancer: Preliminary Results. CoRR abs/1802.08200 (2018) - [i4]Vishwa S. Parekh, Michael A. Jacobs:
MPRAD: A Multiparametric Radiomics Framework. CoRR abs/1809.09973 (2018) - [i3]Vishwa S. Parekh, Michael A. Jacobs:
Radiomic Synthesis Using Deep Convolutional Neural Networks. CoRR abs/1810.11090 (2018) - [i2]Michael A. Jacobs, Christopher Umbricht, Vishwa S. Parekh, Riham El Khouli, Leslie Cope, Katarzyna J. Macura, Susan C. Harvey, Antonio C. Wolff:
Advanced machine learning informatics modeling using clinical and radiological imaging metrics for characterizing breast tumor characteristics with the OncotypeDX gene array. CoRR abs/1811.03218 (2018) - 2016
- [c2]Vishwa S. Parekh, Michael A. Jacobs
A multidimensional data visualization and clustering method: Consensus similarity mapping. ISBI 2016: 420-423 - [i1]Vishwa S. Parekh, Jeremy R. Jacobs, Michael A. Jacobs:
Unsupervised Non Linear Dimensionality Reduction Machine Learning methods applied to Multiparametric MRI in cerebral ischemia: Preliminary Results. CoRR abs/1606.03788 (2016) - 2014
- [c1]Vishwa S. Parekh, Jeremy R. Jacobs, Michael A. Jacobs
Unsupervised nonlinear dimensionality reduction machine learning methods applied to multiparametric MRI in cerebral ischemia: preliminary results. Image Processing 2014: 90342O
Coauthor Index
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