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ORCIDHua Li 0003
- > Home > Persons > Hua Li 0003
Publications
- 2023
[c30]Kaiyan Li, Weimin Zhou, Hua Li, Mark A. Anastasio:
Estimating task-based performance bounds for image reconstruction methods by use of learned-ideal observers. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2023- [i16]Rucha Deshpande, Muzaffer Özbey, Hua Li, Mark A. Anastasio, Frank J. Brooks:
Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context. CoRR abs/2309.10817 (2023) - 2022
- [j15]Kaiyan Li
, Weimin Zhou, Hua Li, Mark A. Anastasio:
A Hybrid Approach for Approximating the Ideal Observer for Joint Signal Detection and Estimation Tasks by Use of Supervised Learning and Markov-Chain Monte Carlo Methods. IEEE Trans. Medical Imaging 41(5): 1114-1124 (2022) - [c29]Zong Fan, Varun A. Kelkar, Mark A. Anastasio, Hua Li:
Application of DatasetGAN in medical imaging: preliminary studies. Medical Imaging: Image Processing 2022 - [c28]Kaiyan Li, Hua Li, Mark A. Anastasio:
A task-informed model training method for deep neural network-based image denoising. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2022 - [i15]Zong Fan, Varun A. Kelkar, Mark A. Anastasio, Hua Li:
Application of DatasetGAN in medical imaging: preliminary studies. CoRR abs/2202.13463 (2022) - 2021
- [j12]Shenghua He, Kyaw Thu Minn, Lilianna Solnica-Krezel, Mark A. Anastasio, Hua Li:
Deeply-supervised density regression for automatic cell counting in microscopy images. Medical Image Anal. 68: 101892 (2021) - [j11]Kaiyan Li, Weimin Zhou, Hua Li, Mark A. Anastasio:
Assessing the Impact of Deep Neural Network-Based Image Denoising on Binary Signal Detection Tasks. IEEE Trans. Medical Imaging 40(9): 2295-2305 (2021) - [c26]Kaiyan Li, Weimin Zhou, Hua Li, Mark A. Anastasio:
Supervised learning-based ideal observer approximation for joint detection and estimation tasks. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2021 - [c25]Kaiyan Li, Weimin Zhou, Hua Li, Mark A. Anastasio:
Task-based performance evaluation of deep neural network-based image denoising. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2021 - [c24]Varun A. Kelkar, Xiaohui Zhang, Jason L. Granstedt, Hua Li, Mark A. Anastasio:
Task-based evaluation of deep image super-resolution in medical imaging. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2021 - [c23]Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Jason L. Granstedt, Hua Li, Mark A. Anastasio:
Advancing the AmbientGAN for learning stochastic object models. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2021 - [i12]Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Jason L. Granstedt, Hua Li, Mark A. Anastasio:
Advancing the AmbientGAN for learning stochastic object models. CoRR abs/2102.00281 (2021) - [i11]Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio:
Learning stochastic object models from medical imaging measurements by use of advanced AmbientGANs. CoRR abs/2106.14324 (2021) - [i10]Xiaohui Zhang, Varun A. Kelkar, Jason L. Granstedt, Hua Li, Mark A. Anastasio:
Impact of deep learning-based image super-resolution on binary signal detection. CoRR abs/2107.02338 (2021) - 2020
- [j9]Weimin Zhou, Hua Li, Mark A. Anastasio:
Approximating the Ideal Observer for Joint Signal Detection and Localization Tasks by use of Supervised Learning Methods. IEEE Trans. Medical Imaging 39(12): 3992-4000 (2020) - [c21]Shenghua He, Weimin Zhou, Hua Li, Mark A. Anastasio:
Learning numerical observers using unsupervised domain adaptation. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2020: 113160W - [c20]Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio:
Progressively-Growing AmbientGANs for learning stochastic object models from imaging measurements. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2020: 113160Q - [i9]Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio:
Progressively-Growing AmbientGANs For Learning Stochastic Object Models From Imaging Measurements. CoRR abs/2001.09523 (2020) - [i8]Shenghua He, Weimin Zhou, Hua Li, Mark A. Anastasio:
Learning Numerical Observers using Unsupervised Domain Adaptation. CoRR abs/2002.03763 (2020) - [i7]Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio:
Learning stochastic object models from medical imaging measurements using Progressively-Growing AmbientGANs. CoRR abs/2006.00033 (2020) - [i6]Weimin Zhou, Hua Li, Mark A. Anastasio:
Approximating the Ideal Observer for joint signal detection and localization tasks by use of supervised learning methods. CoRR abs/2006.00112 (2020) - [i5]Shenghua He, Kyaw Thu Minn, Lilianna Solnica-Krezel, Mark A. Anastasio, Hua Li:
Deeply-Supervised Density Regression for Automatic Cell Counting in Microscopy Images. CoRR abs/2011.03683 (2020) - 2019
- [j6]Weimin Zhou, Hua Li, Mark A. Anastasio:
Approximating the Ideal Observer and Hotelling Observer for Binary Signal Detection Tasks by Use of Supervised Learning Methods. IEEE Trans. Medical Imaging 38(10): 2456-2468 (2019) - [c19]Shenghua He, Kyaw Thu Minn, Lilianna Solnica-Krezel, Mark A. Anastasio, Hua Li:
Automatic microscopic cell counting by use of deeply-supervised density regression model. Medical Imaging: Digital Pathology 2019: 109560L - [c18]Shenghua He, Kyaw Thu Minn, Lilianna Solnica-Krezel, Hua Li, Mark A. Anastasio:
Automatic microscopic cell counting by use of unsupervised adversarial domain adaptation and supervised density regression. Medical Imaging: Digital Pathology 2019: 1095604 - [c17]Weimin Zhou, Hua Li, Mark A. Anastasio:
Learning the Hotelling observer for SKE detection tasks by use of supervised learning methods. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2019: 1095208 - [i4]Shenghua He, Kyaw Thu Minn, Lilianna Solnica-Krezel, Hua Li, Mark A. Anastasio:
Automatic microscopic cell counting by use of unsupervised adversarial domain adaptation and supervised density regression. CoRR abs/1903.00388 (2019) - [i3]Shenghua He, Kyaw Thu Minn, Lilianna Solnica-Krezel, Mark A. Anastasio, Hua Li:
Automatic microscopic cell counting by use of deeply-supervised density regression model. CoRR abs/1903.01084 (2019) - [i2]Weimin Zhou, Hua Li, Mark A. Anastasio:
Approximating the Ideal Observer and Hotelling Observer for binary signal detection tasks by use of supervised learning methods. CoRR abs/1905.06330 (2019) - 2018
- [j5]Jian Wu, Thomas R. Mazur, Su Ruan, Chunfeng Lian, Nalini Daniel, Hilary Lashmett, Laura Ochoa, Imran Zoberi, Mark A. Anastasio, H. Michael Gach, Sasa Mutic, Maria Thomas, Hua Li:
A deep Boltzmann machine-driven level set method for heart motion tracking using cine MRI images. Medical Image Anal. 47: 68-80 (2018) - [c15]Jian Wu, Su Ruan, Chunfeng Lian, Sasa Mutic, Mark A. Anastasio, Hua Li:
Active learning with noise modeling for medical image annotation. ISBI 2018: 298-301 - [c14]Jian Wu, Su Ruan, Thomas R. Mazur, Nalini Daniel, Hilary Lashmett, Laura Ochoa, Imran Zoberi, Chunfeng Lian, H. Michael Gach, Sasa Mutic, Maria Thomas, Mark A. Anastasio, Hua Li:
Heart motion tracking on cine MRI based on a deep Boltzmann machine-driven level set method. ISBI 2018: 1153-1156 - [c13]Shenghua He, Jie Zheng, Akiko Maehara, Gary S. Mintz, Dalin Tang, Mark A. Anastasio, Hua Li:
Convolutional neural network based automatic plaque characterization for intracoronary optical coherence tomography images. Medical Imaging: Image Processing 2018: 1057432 - [i1]Shenghua He, Jie Zheng, Akiko Maehara, Gary S. Mintz, Dalin Tang, Mark A. Anastasio, Hua Li:
Convolutional neural network based automatic plaque characterization from intracoronary optical coherence tomography images. CoRR abs/1807.03613 (2018)
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