4.2k citations · 6.5k across the 42 of their papers we have counts for
7 papers · 1 filter
Deep Learning Based Detection of Enlarged Perivascular Spaces on Brain MRI
Tanweer Rashid, Hangfan Liu, Jeffrey B. Ware +12
BACKGROUND AND PURPOSE: Deep learning has been demonstrated effective in many neuroimaging applications. However, in many scenarios, the number of imaging sequences capturing infor…
Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience
Rongguang Wang, Vishnu Bashyam, Zhijian Yang +10
Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to a broader family called…
Surreal-GAN:Semi-Supervised Representation Learning via GAN for uncovering heterogeneous disease-related imaging patterns
Zhijian Yang, Junhao Wen, Christos Davatzikos
A plethora of machine learning methods have been applied to imaging data, enabling the construction of clinically relevant imaging signatures of neurological and neuropsychiatric d…
Bias in Machine Learning Models Can Be Significantly Mitigated by Careful Training: Evidence from Neuroimaging Studies
Rongguang Wang, Pratik Chaudhari, Christos Davatzikos
Despite the great promise that machine learning has offered in many fields of medicine, it has also raised concerns about potential biases and poor generalization across genders, a…
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Sarthak Pati, Ujjwal Baid, Brandon Edwards +276
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…
Robust Hierarchical Patterns for identifying MDD patients: A Multisite Study
Dushyant Sahoo, Mathilde Antoniades, Cynthia H. Y. Fu +1
Many supervised machine learning frameworks have been proposed for disease classification using functional magnetic resonance imaging (fMRI) data, producing important biomarkers. M…