1H-MRS and machine learning for predicting voxel-wise histopathology of tumor cells in newly-diagnosed glioma patients

Contrastive Learning of Inter-domain Similarity for Unsupervised Multi-modality Deformable Registration

Deep generative model for learning tractography streamline embeddings based on a Convolutional Variational Autoencoder

Deep learning-based brain MRI reconstruction with realistic noise

Author:Quan Dou  Xue Feng  Craig Meyer  

Session Type:Online Gather.town Pitches  

Session Date:Monday, 09 May 2022  

Topic:Module 5: Machine Learning/Artificial Intelligence  

Session Name:Machine Learning & Artificial Intelligence II  

Program Number:3470  

Room Session:5  

Institution:University of Virginia  

Deep CNNs with Physical Constraints for simultaneous Multi-tissue Segmentation and Multi-parameter Quantification (MSMQ-Net) of Knee

DL-MOTIF:DeepLearning BasedMotionTransformationIntegratedForward-Fourier Reconstruction for Free-Breathing Liver DCE-MRI

Mammography Lesion ROI Drawing Guided by Breast MRI MIP to Extract Features from Corresponding Lesions to Build Radiomics Diagnostic Models

Perivascular Space Quantification with Deep Learning synthesized T2 from T1w and FLAIR images

Predicting Breath-Hold Liver Diffusion MRI from Free-Breathing Datausing a Convolutional Neural Network (CNN)

Radiomics to Predict Pathological Complete Response in Patients with Triple Negative Breast Cancer

Self-supervised and physics informed deep learning model for prediction of multiple tissue parameters from MR Fingerprinting data

Unsupervised Domain Adaptation for Neural Network Enhanced Turbo Spin Echo Imaging

Using deep learning to generate missing anatomical imaging contrasts required for lesion segmentation in patients with glioma.