远程医疗应用的多模态深度生成模型

Catherine Ordun
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引用次数: 0

摘要

可视到热(VT)人脸翻译是一个尚未得到充分研究的图像到图像翻译问题,它为传统的热传感器提供了一种人工智能支持的替代方案。在三个阶段,我的博士提案探索开发可应用于远程医疗应用的多模态深度生成解决方案。其中包括一种新型热面对比GAN (TFC-GAN)的贡献,混合扩散GAN模型的探索,在美国国立卫生研究院的实际临床热数据上的应用,以及在异构数据设置中探索联邦学习(FL)策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multimodal Deep Generative Models for Remote Medical Applications
Visible-to-Thermal (VT) face translation is an under-studied problem of image-to-image translation that offers an AI-enabled alternative to traditional thermal sensors. Over three phases, my Doctoral Proposal explores developing multimodal deep generative solutions that can be applied towards telemedicine applications. These include the contribution of a novel Thermal Face Contrastive GAN (TFC-GAN), exploration of hybridized diffusion-GAN models, application on real clinical thermal data at the National Institutes of Health, and exploration of strategies for Federated Learning (FL) in heterogenous data settings.
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