399 citations · 427 across the 15 of their papers we have counts for
15 papers
BSM loss: A superior way in modeling aleatory uncertainty of fine_grained classification
Shuang Ge, Kehong Yuan, Maokun Han +3
Artificial intelligence(AI)-assisted method had received much attention in the risk field such as disease diagnosis. Different from the classification of disease types, it is a fin…
MIPR:Automatic Annotation of Medical Images with Pixel Rearrangement
Pingping Dai, Haiming Zhu, Shuang Ge +4
Most of the state-of-the-art semantic segmentation reported in recent years is based on fully supervised deep learning in the medical domain. How?ever, the high-quality annotated d…
Soft-CP: A Credible and Effective Data Augmentation for Semantic Segmentation of Medical Lesions
Pingping Dai, Licong Dong, Ruihan Zhang +3
The medical datasets are usually faced with the problem of scarcity and data imbalance. Moreover, annotating large datasets for semantic segmentation of medical lesions is domain-k…
AI assisted method for efficiently generating breast ultrasound screening reports
Shuang Ge, Qiongyu Ye, Wenquan Xie +4
Background: Ultrasound is one of the preferred choices for early screening of dense breast cancer. Clinically, doctors have to manually write the screening report which is time-con…
Knowledge AI: New Medical AI Solution for Medical image Diagnosis
Yingni Wang, Shuge Lei, Jian Dai +1
The implementation of medical AI has always been a problem. The effect of traditional perceptual AI algorithm in medical image processing needs to be improved. Here we propose a me…
More Reliable AI Solution: Breast Ultrasound Diagnosis Using Multi-AI Combination
Jian Dai, Shuge Lei, Licong Dong +4
Objective: Breast cancer screening is of great significance in contemporary women's health prevention. The existing machines embedded in the AI system do not reach the accuracy tha…
Deformable Registration Using Average Geometric Transformations for Brain MR Images
Yongpei Zhu, Zicong Zhou, Guojun Liao +1
Accurate registration of medical images is vital for doctor's diagnosis and quantitative analysis. In this paper, we propose a new deformable medical image registration method base…
Facial Expression Recognition Research Based on Deep Learning
Yongpei Zhu, Hongwei Fan, Kehong Yuan
With the development of deep learning, the structure of convolution neural network is becoming more and more complex and the performance of object recognition is getting better. Ho…
Medical Image Super-Resolution Using a Generative Adversarial Network
Yongpei Zhu, Xuesheng Zhang, Kehong Yuan
During the growing popularity of electronic medical records, electronic medical record (EMR) data has exploded increasingly. It is very meaningful to retrieve high quality EMR in m…
Efforts estimation of doctors annotating medical image
Yang Deng, Yao Sun, Yongpei Zhu +8
Accurate annotation of medical image is the crucial step for image AI clinical application. However, annotating medical image will incur a great deal of annotation effort and expen…
The Method of Multimodal MRI Brain Image Segmentation Based on Differential Geometric Features
Yongpei Zhu, Zicong Zhou, Guojun Liao +2
Accurate segmentation of brain tissue in magnetic resonance images (MRI) is a diffcult task due to different types of brain abnormalities. Using information and features from multi…
A Multi-channel Network with Image Retrieval for Accurate Brain Tissue Segmentation
Yao Sun, Yang Deng, Yue Xu +3
Magnetic Resonance Imaging (MRI) is widely used in the pathological and functional studies of the brain, such as epilepsy, tumor diagnosis, etc. Automated accurate brain tissue seg…
A Strategy of MR Brain Tissue Images' Suggestive Annotation Based on Modified U-Net
Yang Deng, Yao Sun, Yongpei Zhu +3
Accurate segmentation of MR brain tissue is a crucial step for diagnosis,surgical planning, and treatment of brain abnormalities. However,it is a time-consuming task to be performe…
DASN:Data-Aware Skilled Network for Accurate MR Brain Tissue Segmentation
Yang Deng, Yao Sun, Yongpei Zhu +3
Accurate segmentation of MR brain tissue is a crucial step for diagnosis, surgical planning, and treatment of brain abnormalities. Automatic and reliable segmenta-tion methods are…
GPU-based Low Dose CT Reconstruction via Edge-preserving Total Variation Regularization
Zhen Tian, Xun Jia, Kehong Yuan +2
High radiation dose in CT scans increases a lifetime risk of cancer and has become a major clinical concern. Recently, iterative reconstruction algorithms with Total Variation (TV)…