Federated learning (FL), a paradigm that enables privacy-protected collaborative learning among different institutions, is a promising solution to this challenge.
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) symposium 2021.
These measurements are typically synthesized from images using a fixed physical model of the measurement process, which hinders the generalization capability of models to unknown measurement processes.
The method differs fundamentally from previous deep learning-based image reconstruction approaches in that NeRP exploits the internal information in an image prior, and the physics of the sparsely sampled measurements to produce a representation of the unknown subject.
Deep learning affords enormous opportunities to augment the armamentarium of biomedical imaging, albeit its design and implementation have potential flaws.
In recent years, the growing number of medical imaging studies is placing an ever-increasing burden on radiologists.
no code implementations • 10 Jul 2020 • Liyue Shen, Wentao Zhu, Xiaosong Wang, Lei Xing, John M. Pauly, Baris Turkbey, Stephanie Anne Harmon, Thomas Hogue Sanford, Sherif Mehralivand, Peter Choyke, Bradford Wood, Daguang Xu
Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e. g., brain tumor segmentation from multi-parametric magnetic resonance imaging (MRI).
We subsequently leverage a particle stochastic gradient descent (SGD) method to solve finite dimensional optimization problems.
We subsequently leverage a particle stochastic gradient descent (SGD) method to solve the derived finite dimensional optimization problem.
Our method uses Q-learning to learn a data labeling policy on a small labeled training dataset, and then uses this to automatically label noisy web data for new visual concepts.
As a minor contribution, inspired by recent advances in large-scale image search, this paper proposes an unsupervised Bag-of-Words descriptor.
Ranked #84 on Person Re-Identification on DukeMTMC-reID
In the light of recent advances in image search, this paper proposes to treat person re-identification as an image search problem.