65 citations · 119 across the 8 of their papers we have counts for
8 papers
Accurate and Robust Lesion RECIST Diameter Prediction and Segmentation with Transformers
Youbao Tang, Ning Zhang, Yirui Wang +4
Automatically measuring lesion/tumor size with RECIST (Response Evaluation Criteria In Solid Tumors) diameters and segmentation is important for computer-aided diagnosis. Although…
PieTrack: An MOT solution based on synthetic data training and self-supervised domain adaptation
Yirui Wang, Shenghua He, Youbao Tang +8
In order to cope with the increasing demand for labeling data and privacy issues with human detection, synthetic data has been used as a substitute and showing promising results in…
Artificial confocal microscopy for deep label-free imaging
Xi Chen, Mikhail E. Kandel, Shenghua He +8
Widefield microscopy methods applied to optically thick specimens are faced with reduced contrast due to spatial crosstalk, in which the signal at each point is the result of a sup…
Deeply-Supervised Density Regression for Automatic Cell Counting in Microscopy Images
Shenghua He, Kyaw Thu Minn, Lilianna Solnica-Krezel +2
Accurately counting the number of cells in microscopy images is required in many medical diagnosis and biological studies. This task is tedious, time-consuming, and prone to subjec…
Learning Numerical Observers using Unsupervised Domain Adaptation
Shenghua He, Weimin Zhou, Hua Li +1
Medical imaging systems are commonly assessed by use of objective image quality measures. Supervised deep learning methods have been investigated to implement numerical observers f…
Automatic microscopic cell counting by use of deeply-supervised density regression model
Shenghua He, Kyaw Thu Minn, Lilianna Solnica-Krezel +2
Accurately counting cells in microscopic images is important for medical diagnoses and biological studies, but manual cell counting is very tedious, time-consuming, and prone to su…
Automatic microscopic cell counting by use of unsupervised adversarial domain adaptation and supervised density regression
Shenghua He, Kyaw Thu Minn, Lilianna Solnica-Krezel +2
Accurate cell counting in microscopic images is important for medical diagnoses and biological studies. However, manual cell counting is very time-consuming, tedious, and prone to…
Convolutional neural network based automatic plaque characterization from intracoronary optical coherence tomography images
Shenghua He, Jie Zheng, Akiko Maehara +4
Optical coherence tomography (OCT) can provide high-resolution cross-sectional images for analyzing superficial plaques in coronary arteries. Commonly, plaque characterization usin…