7 citations · 17 across the 4 of their papers we have counts for
5 papers
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…