Boundary-based warping of brain MR images. - Amir Ghanei; Hamid Soltanian-Zadeh; Michael A. Jacobs (Image Processing, 2000)

Volumenerhaltende elastische Registrierung. Evaluierung mit klinischen MR-Mammographien. - Torsten Rohlfing; Calvin R. Maurer Jr.; David A. Bluemke; Michael A. Jacobs (Bildverarbeitung für die Medizin, 2003)

An Alternating-Constraints Algorithm for Volume-Preserving Non-rigid Registration of Contrast-Enhanced MR Breast Images. - Torsten Rohlfing; Calvin R. Maurer Jr.; David A. Bluemke; Michael A. Jacobs (WBIR, 2003)

Volume-Preserving Non-Rigid Registration of MR Breast Images Using Free-Form Deformation with an Incompressibility Constraint. - Torsten Rohlfing; Calvin R. Maurer Jr.; David A. Bluemke; Michael A. Jacobs (IEEE Trans. Medical Imaging, 2003)

Finding the Optimal Compression Level for Strain-Encoded (SENC) Breast MRI; Simulations and Phantom Experiments. - Ahmed A. Harouni; Michael A. Jacobs; Nael F. Osman (MICCAI (1), 2011)

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)

DreamNLP: Novel NLP System for Clinical Report Metadata Extraction using Count Sketch Data Streaming Algorithm: Preliminary Results. - Sanghyun Choi; Nikita Ivkin; Vladimir Braverman; 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)

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)

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

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)

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)

Predicting Hypersonic Glide Vehicle Behavior With Stochastic Grammars. - Emily R. Bartusiak; Michael A. Jacobs; Moses W. Chan; Mary L. Comer; Edward J. Delp (IEEE Trans. Aerosp. Electron. Syst., 2024)

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