107 citations · 184 across the 18 of their papers we have counts for
6 papers · 1 filter
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…
A Parallel Down-Up Fusion Network for Salient Object Detection in Optical Remote Sensing Images
Chongyi Li, Runmin Cong, Chunle Guo +4
The diverse spatial resolutions, various object types, scales and orientations, and cluttered backgrounds in optical remote sensing images (RSIs) challenge the current salient obje…
Approximating the Ideal Observer for joint signal detection and localization tasks by use of supervised learning methods
Weimin Zhou, Hua Li, Mark A. Anastasio
Medical imaging systems are commonly assessed and optimized by use of objective measures of image quality (IQ). The Ideal Observer (IO) performance has been advocated to provide a…
Learning stochastic object models from medical imaging measurements using Progressively-Growing AmbientGANs
Weimin Zhou, Sayantan Bhadra, Frank J. Brooks +2
It has been advocated that medical imaging systems and reconstruction algorithms should be assessed and optimized by use of objective measures of image quality that quantify the pe…
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…
Progressively-Growing AmbientGANs For Learning Stochastic Object Models From Imaging Measurements
Weimin Zhou, Sayantan Bhadra, Frank J. Brooks +2
The objective optimization of medical imaging systems requires full characterization of all sources of randomness in the measured data, which includes the variability within the en…