Multimodal classification of Alzheimer's disease and mild cognitive impairment
Infant Brain Atlases from Neonates to 1- and 2-Year-Olds
It is expected that the proposed infant 0–1–2 brain atlases would be significantly conducive to structural and functional studies of the infant brains.
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19
This review paper covers the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up, and particularly focuses on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals.
Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease
Lung Infection Quantification of COVID-19 in CT Images with Deep Learning
A deep learning (DL) based segmentation system is developed to automatically quantify infection regions of interest (ROIs) and their volumetric ratios w.r.t. the lung and possible applications, including but not limited to analysis of follow-up CT scans and infection distributions in the lobes and segments correlated with clinical findings were discussed.
Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection
Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging
Family Poverty Affects the Rate of Human Infant Brain Growth
Differences in brain growth were found to vary with socioeconomic status (SES), with children from lower-income households having slower trajectories of growth during infancy and early childhood.
Contour Knowledge Transfer for Salient Object Detection
- Xin Li, F. Yang, Hong Cheng, W. Liu, D. Shen
- Computer ScienceEuropean Conference on Computer Vision
- 8 September 2018
A contour-to-saliency transferring method to automatically generate salient object masks which can be used to train the saliency branch from outputs of the contour branch, and introduces a novel alternating training pipeline to gradually update the network parameters.
Evidence on the emergence of the brain's default network from 2-week-old to 2-year-old healthy pediatric subjects
- Wei Gao, Hongtu Zhu, Weili Lin
- Biology, PsychologyProceedings of the National Academy of Sciences
- 21 April 2009
The temporal evolution of the default network in a critical, previously unstudied, period of early human brain development is described and new insights into the emergence of brain default network are offered.