Can Son Khoo

TL;DR: This diagnostic accuracy study evaluates whether artificial intelligence can overcome human mammography interpretation limits with a rigorous, unbiased evaluation of machine learning algorithms.

TL;DR: Results show that data harmonization algorithms can reduce the cross-scanner and cross-protocol variabilities to a similar level as scan-rescan variability using the same scanner and protocol.

TL;DR: The results show that cross-vendor harmonization and enhancement can be performed by using suitable computational algorithms such as deep convolutional neural networks and parametric models for multi-shell diffusion MRI signals also provide reliable performances.

TL;DR: This paper introduces the Multi Stage Prediction (MSP) Network, a MTL framework that incorporates neural networks of potentially disparate architectures, trained for different individual acquisition platforms, into a larger architecture that is refined in unison.

TL;DR: In this paper, a multi-stage prediction (MSP) network is proposed to integrate information from multiple acquisition platforms and improve the predictive performance and learning efficiency of the harmonization model.