DART: Depth-Enhanced Accurate and Real-Time Background Matting

no code implementations • 24 Feb 2024 • Hanxi Li, Guofeng Li, Bo Li, Lin Wu, Yan Cheng

In this paper, we leverage the rich depth information provided by the RGB-Depth (RGB-D) cameras to enhance background matting performance in real-time, dubbed DART.

Bayesian Inference Edge-computing +1

Empowering Federated Learning for Massive Models with NVIDIA FLARE

no code implementations • 12 Feb 2024 • Holger R. Roth, Ziyue Xu, Yuan-Ting Hsieh, Adithya Renduchintala, Isaac Yang, Zhihong Zhang, Yuhong Wen, Sean Yang, Kevin Lu, Kristopher Kersten, Camir Ricketts, Daguang Xu, Chester Chen, Yan Cheng, Andrew Feng

In the ever-evolving landscape of artificial intelligence (AI) and large language models (LLMs), handling and leveraging data effectively has become a critical challenge.

Federated Learning

NVIDIA FLARE: Federated Learning from Simulation to Real-World

1 code implementation • 24 Oct 2022 • Holger R. Roth, Yan Cheng, Yuhong Wen, Isaac Yang, Ziyue Xu, Yuan-Ting Hsieh, Kristopher Kersten, Ahmed Harouni, Can Zhao, Kevin Lu, Zhihong Zhang, Wenqi Li, Andriy Myronenko, Dong Yang, Sean Yang, Nicola Rieke, Abood Quraini, Chester Chen, Daguang Xu, Nic Ma, Prerna Dogra, Mona Flores, Andrew Feng

Federated learning (FL) enables building robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without centralizing the data.

Federated Learning Privacy Preserving

Federated Learning for Breast Density Classification: A Real-World Implementation

no code implementations • 3 Sep 2020 • Holger R. Roth, Ken Chang, Praveer Singh, Nir Neumark, Wenqi Li, Vikash Gupta, Sharut Gupta, Liangqiong Qu, Alvin Ihsani, Bernardo C. Bizzo, Yuhong Wen, Varun Buch, Meesam Shah, Felipe Kitamura, Matheus Mendonça, Vitor Lavor, Ahmed Harouni, Colin Compas, Jesse Tetreault, Prerna Dogra, Yan Cheng, Selnur Erdal, Richard White, Behrooz Hashemian, Thomas Schultz, Miao Zhang, Adam McCarthy, B. Min Yun, Elshaimaa Sharaf, Katharina V. Hoebel, Jay B. Patel, Bryan Chen, Sean Ko, Evan Leibovitz, Etta D. Pisano, Laura Coombs, Daguang Xu, Keith J. Dreyer, Ittai Dayan, Ram C. Naidu, Mona Flores, Daniel Rubin, Jayashree Kalpathy-Cramer

Building robust deep learning-based models requires large quantities of diverse training data.

breast density classification Classification +2

Training Models 20X Faster in Medical Image Analysis

no code implementations • MIDL 2019 • Dong Yang, Holger Roth, Xiaosong Wang, Ziyue Xu, Yan Cheng, Daguang Xu

Analyzing high-dimensional medical images (2D/3D/4D CT, MRI, histopathological images, etc.)

Privacy-preserving Federated Brain Tumour Segmentation

no code implementations • 2 Oct 2019 • Wenqi Li, Fausto Milletarì, Daguang Xu, Nicola Rieke, Jonny Hancox, Wentao Zhu, Maximilian Baust, Yan Cheng, Sébastien Ourselin, M. Jorge Cardoso, Andrew Feng

Due to medical data privacy regulations, it is often infeasible to collect and share patient data in a centralised data lake.

Federated Learning Privacy Preserving +1