Approximating the Ideal Observer and Hotelling Observer for binary signal detection tasks by use of supervised learning methods

15 May 2019

Papers citing "Approximating the Ideal Observer and Hotelling Observer for binary signal detection tasks by use of supervised learning methods"

18 / 18 papers shown

Title
Using gradient of Lagrangian function to compute efficient channels for the ideal observer Weimin Zhou 71 0 0 31 Jan 2025
Estimating Task-based Performance Bounds for Accelerated MRI Image Reconstruction Methods by Use of Learned-Ideal Observers Kaiyan Li Prabhat Kc Hua Li Kyle J. Myers M. Anastasio Rongping Zeng OOD 29 1 0 17 Jan 2025
Report on the AAPM Grand Challenge on deep generative modeling for learning medical image statistics Rucha Deshpande Varun A. Kelkar Dimitrios S. Gotsis Prabhat Kc Rongping Zeng Kyle J. Myers F. Brooks M. Anastasio MedIm 21 4 0 03 May 2024
Enhancing signal detectability in learning-based CT reconstruction with a model observer inspired loss function Megan Lantz E. Sidky Ingrid S. Reiser Xiaochuan Pan Gregory Ongie 42 1 0 15 Feb 2024
Ambient-Pix2PixGAN for Translating Medical Images from Noisy Data Wentao Chen Xichen Xu Jie Luo Weimin Zhou MedIm 19 3 0 02 Feb 2024
AmbientCycleGAN for Establishing Interpretable Stochastic Object Models Based on Mathematical Phantoms and Medical Imaging Measurements Xichen Xu Wentao Chen Weimin Zhou MedIm DiffM 16 2 0 02 Feb 2024
Ideal Observer Computation by Use of Markov-Chain Monte Carlo with Generative Adversarial Networks Weimin Zhou Umberto Villa M. Anastasio 22 7 0 02 Apr 2023
A deep Q-learning method for optimizing visual search strategies in backgrounds of dynamic noise Weimin Zhou M. Eckstein 12 3 0 28 Jan 2022
Impact of deep learning-based image super-resolution on binary signal detection Xiaohui Zhang Varun A. Kelkar Jason L. Granstedt Hua Li M. Anastasio MedIm 8 23 0 06 Jul 2021
Learning stochastic object models from medical imaging measurements by use of advanced ambient generative adversarial networks Weimin Zhou Sayantan Bhadra F. Brooks Hua Li M. Anastasio GAN MedIm 18 17 0 27 Jun 2021
Advancing the AmbientGAN for learning stochastic object models Weimin Zhou Sayantan Bhadra F. Brooks Jason L. Granstedt Hua Li M. Anastasio GAN MedIm 11 3 0 30 Jan 2021
Approximating the Ideal Observer for joint signal detection and localization tasks by use of supervised learning methods Weimin Zhou Hua Li M. Anastasio 6 29 0 29 May 2020
Learning stochastic object models from medical imaging measurements using Progressively-Growing AmbientGANs Weimin Zhou Sayantan Bhadra F. Brooks Hua Li M. Anastasio MedIm 14 5 0 29 May 2020
Approximating the Hotelling Observer with Autoencoder-Learned Efficient Channels for Binary Signal Detection Tasks Jason L. Granstedt Weimin Zhou M. Anastasio 13 5 0 04 Mar 2020
Learning Numerical Observers using Unsupervised Domain Adaptation Shenghua He Weimin Zhou Hua Li M. Anastasio OOD 6 7 0 03 Feb 2020
Markov-Chain Monte Carlo Approximation of the Ideal Observer using Generative Adversarial Networks Weimin Zhou M. Anastasio 11 21 0 26 Jan 2020
Progressively-Growing AmbientGANs For Learning Stochastic Object Models From Imaging Measurements Weimin Zhou Sayantan Bhadra F. Brooks Hua Li M. Anastasio GAN MedIm 9 6 0 26 Jan 2020
A convolutional neural network reaches optimal sensitivity for detecting some, but not all, patterns Fabian H. Reith B. Wandell 12 2 0 12 Nov 2019