Výsledky vyhledávání - "Jacobs, Michael"

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A framework for dynamically training and adapting deep reinforcement learning models to different, low-compute, and continuously changing radiology deployment environments

Autor: Zheng, Guangyao, Lai, Shuhao, Braverman, Vladimir, Jacobs, Michael A., Parekh, Vishwa S.

While Deep Reinforcement Learning has been widely researched in medical imaging, the training and deployment of these models usually require powerful GPUs. Since imaging environments evolve rapidly and can be generated by edge devices, the algorithm

Externí odkaz: http://arxiv.org/abs/2306.05310

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Multi-environment lifelong deep reinforcement learning for medical imaging

Autor: Zheng, Guangyao, Lai, Shuhao, Braverman, Vladimir, Jacobs, Michael A., Parekh, Vishwa S.

Deep reinforcement learning(DRL) is increasingly being explored in medical imaging. However, the environments for medical imaging tasks are constantly evolving in terms of imaging orientations, imaging sequences, and pathologies. To that end, we deve

Externí odkaz: http://arxiv.org/abs/2306.00188

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Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging

Autor: Zheng, Guangyao, Jacobs, Michael A., Braverman, Vladimir, Parekh, Vishwa S.

Federated learning is a recent development in the machine learning area that allows a system of devices to train on one or more tasks without sharing their data to a single location or device. However, this framework still requires a centralized glob

Externí odkaz: http://arxiv.org/abs/2303.06783

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