84 citations · 261 across the 13 of their papers we have counts for
13 papers
Multi-level Adversarial Spatio-temporal Learning for Footstep Pressure based FoG Detection
Kun Hu, Shaohui Mei, Wei Wang +5
Freezing of gait (FoG) is one of the most common symptoms of Parkinson's disease, which is a neurodegenerative disorder of the central nervous system impacting millions of people a…
DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT
Mingyuan Meng, Bingxin Gu, Lei Bi +3
Nasopharyngeal Carcinoma (NPC) is a malignant epithelial cancer arising from the nasopharynx. Survival prediction is a major concern for NPC patients, as it provides early prognost…
Prediction of 5-year Progression-Free Survival in Advanced Nasopharyngeal Carcinoma with Pretreatment PET/CT using Multi-Modality Deep Learning-based Radiomics
Bingxin Gu, Mingyuan Meng, Lei Bi +3
Objective: Deep Learning-based Radiomics (DLR) has achieved great success in medical image analysis and has been considered a replacement for conventional radiomics that relies on…
Enhancing Medical Image Registration via Appearance Adjustment Networks
Mingyuan Meng, Lei Bi, Michael Fulham +2
Deformable image registration is fundamental for many medical image analyses. A key obstacle for accurate image registration lies in image appearance variations such as the variati…
Deep Multi-Scale Resemblance Network for the Sub-class Differentiation of Adrenal Masses on Computed Tomography Images
Lei Bi, Jinman Kim, Tingwei Su +3
The accurate classification of mass lesions in the adrenal glands (adrenal masses), detected with computed tomography (CT), is important for diagnosis and patient management. Adren…
Semi-supervised estimation of event temporal length for cell event detection
Ha Tran Hong Phan, Ashnil Kumar, David Feng +2
Cell event detection in cell videos is essential for monitoring of cellular behavior over extended time periods. Deep learning methods have shown great success in the detection of…
Automated Segmentation of the Optic Disk and Cup using Dual-Stage Fully Convolutional Networks
Lei Bi, Yuyu Guo, Qian Wang +3
Automated segmentation of the optic cup and disk on retinal fundus images is fundamental for the automated detection / analysis of glaucoma. Traditional segmentation approaches dep…
Improving Automatic Skin Lesion Segmentation using Adversarial Learning based Data Augmentation
Lei Bi, Dagan Feng, Jinman Kim
Segmentation of skin lesions is considered as an important step in computer aided diagnosis (CAD) for automated melanoma diagnosis. In recent years, segmentation methods based on f…
3D Global Convolutional Adversarial Network\\ for Prostate MR Volume Segmentation
Haozhe Jia, Yang Song, Donghao Zhang +5
Advanced deep learning methods have been developed to conduct prostate MR volume segmentation in either a 2D or 3D fully convolutional manner. However, 2D methods tend to have limi…
An unsupervised long short-term memory neural network for event detection in cell videos
Ha Tran Hong Phan, Ashnil Kumar, David Feng +2
We propose an automatic unsupervised cell event detection and classification method, which expands convolutional Long Short-Term Memory (LSTM) neural networks, for cellular events…
Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)
Lei Bi, Jinman Kim, Ashnil Kumar +2
Positron emission tomography (PET) image synthesis plays an important role, which can be used to boost the training data for computer aided diagnosis systems. However, existing ima…
Automatic Liver Lesion Detection using Cascaded Deep Residual Networks
Lei Bi, Jinman Kim, Ashnil Kumar +1
Automatic segmentation of liver lesions is a fundamental requirement towards the creation of computer aided diagnosis (CAD) and decision support systems (CDS). Traditional segmenta…
Morphometry-Based Longitudinal Neurodegeneration Simulation with MR Imaging
Siqi Liu, Sidong Liu, Sonia Pujol +4
We present a longitudinal MR simulation framework which simulates the future neurodegenerative progression by outputting the predicted follow-up MR image and the voxel-based morpho…