107 citations · 199 across the 20 of their papers we have counts for
12 papers · 1 filter
Prior-guided Diffusion Model for Cell Segmentation in Quantitative Phase Imaging
Zhuchen Shao, Mark A. Anastasio, Hua Li
Purpose: Quantitative phase imaging (QPI) is a label-free technique that provides high-contrast images of tissues and cells without the use of chemicals or dyes. Accurate semantic…
Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context
Rucha Deshpande, Muzaffer Özbey, Hua Li +2
Diffusion models have emerged as a popular family of deep generative models (DGMs). In the literature, it has been claimed that one class of diffusion models -- denoising diffusion…
Semi-Supervised Semantic Segmentation of Cell Nuclei via Diffusion-based Large-Scale Pre-Training and Collaborative Learning
Zhuchen Shao, Sourya Sengupta, Hua Li +1
Automated semantic segmentation of cell nuclei in microscopic images is crucial for disease diagnosis and tissue microenvironment analysis. Nonetheless, this task presents challeng…
A quality assurance framework for real-time monitoring of deep learning segmentation models in radiotherapy
Xiyao Jin, Yao Hao, Jessica Hilliard +5
To safely deploy deep learning models in the clinic, a quality assurance framework is needed for routine or continuous monitoring of input-domain shift and the models' performance…
On the impact of incorporating task-information in learning-based image denoising
Kaiyan Li, Hua Li, Mark A. Anastasio
A variety of deep neural network (DNN)-based image denoising methods have been proposed for use with medical images. These methods are typically trained by minimizing loss function…
Impact of deep learning-based image super-resolution on binary signal detection
Xiaohui Zhang, Varun A. Kelkar, Jason Granstedt +2
Deep learning-based image super-resolution (DL-SR) has shown great promise in medical imaging applications. To date, most of the proposed methods for DL-SR have only been assessed…
Learning stochastic object models from medical imaging measurements by use of advanced ambient generative adversarial networks
Weimin Zhou, Sayantan Bhadra, Frank J. Brooks +2
Purpose: To objectively assess new medical imaging technologies via computer-simulations, it is important to account for the variability in the ensemble of objects to be imaged. Th…
Assessing the Impact of Deep Neural Network-based Image Denoising on Binary Signal Detection Tasks
Kaiyan Li, Weimin Zhou, Hua Li +1
A variety of deep neural network (DNN)-based image denoising methods have been proposed for use with medical images. Traditional measures of image quality (IQ) have been employed t…
Advancing the AmbientGAN for learning stochastic object models
Weimin Zhou, Sayantan Bhadra, Frank J. Brooks +3
Medical imaging systems are commonly assessed and optimized by use of objective-measures of image quality (IQ) that quantify the performance of an observer at specific tasks. Varia…
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 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…
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…