25 citations · 40 across the 9 of their papers we have counts for
9 papers
Medical Image Segmentation with Domain Adaptation: A Survey
Yuemeng Li, Yong Fan
Deep learning (DL) has shown remarkable success in various medical imaging data analysis applications. However, it remains challenging for DL models to achieve good generalization,…
Versatile Medical Image Segmentation Learned from Multi-Source Datasets via Model Self-Disambiguation
Xiaoyang Chen, Hao Zheng, Yuemeng Li +4
A versatile medical image segmentation model applicable to images acquired with diverse equipment and protocols can facilitate model deployment and maintenance. However, building s…
Learning Apparent Diffusion Coefficient Maps from Accelerated Radial k-Space Diffusion-Weighted MRI in Mice using a Deep CNN-Transformer Model
Yuemeng Li, Miguel Romanello Joaquim, Stephen Pickup +3
Purpose: To accelerate radially sampled diffusion weighted spin-echo (Rad-DW-SE) acquisition method for generating high quality apparent diffusion coefficient (ADC) maps. Methods:…
Adaptive convolutional neural networks for k-space data interpolation in fast magnetic resonance imaging
Tianming Du, Honggang Zhang, Yuemeng Li +2
Deep learning in k-space has demonstrated great potential for image reconstruction from undersampled k-space data in fast magnetic resonance imaging (MRI). However, existing deep l…
ACEnet: Anatomical Context-Encoding Network for Neuroanatomy Segmentation
Yuemeng Li, Hongming Li, Yong Fan
Segmentation of brain structures from magnetic resonance (MR) scans plays an important role in the quantification of brain morphology. Since 3D deep learning models suffer from hig…
Context-endcoding for neural network based skull stripping in magnetic resonance imaging
Zhen Liu, Borui Xiao, Yuemeng Li +1
Skull stripping is usually the first step for most brain analysisprocess in magnetic resonance images. A lot of deep learn-ing neural network based methods have been developed toac…
Feature-Fused Context-Encoding Network for Neuroanatomy Segmentation
Yuemeng Li, Hangfan Liu, Hongming Li +1
Automatic segmentation of fine-grained brain structures remains a challenging task. Current segmentation methods mainly utilize 2D and 3D deep neural networks. The 2D networks take…
DeepSEED: 3D Squeeze-and-Excitation Encoder-Decoder Convolutional Neural Networks for Pulmonary Nodule Detection
Yuemeng Li, Yong Fan
Pulmonary nodule detection plays an important role in lung cancer screening with low-dose computed tomography (CT) scans. It remains challenging to build nodule detection deep lear…
A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying Abnormalities
Bo Zhou, Yuemeng Li, Jiangcong Wang
We present a weakly supervised deep learning model for classifying thoracic diseases and identifying abnormalities in chest radiography. In this work, instead of learning from medi…