Medical Imaging: Image Perception, Observer Performance, and Technology Assessment

Keyphrases

Publications

2020

Abdulaziz S. Alshabibi, Moayyad E. Suleiman, Kriscia A. Tapia, Robert Heard, Patrick C. Brennan
Effect of time of day on radiology image interpretations. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Jason L. Granstedt, Weimin Zhou, Mark A. Anastasio
Learning efficient channels with a dual loss autoencoder. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Hui Shi, Grace J. Gang, Junyuan Li, Eleni Liapi, Craig K. Abbey, J. Webster Stayman
Performance assessment of texture reproduction in high-resolution CT. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Dimitar Petrov, Nicholas Marshall, Liesbeth Vancoillie, Lesley Cockmartin, Hilde Bosmans
Anthropomorphic ResNet18 for multi-vendor DBT image quality evaluation. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Craig K. Abbey, Frank W. Samuelson, Rongping Zeng, John M. Boone, Miguel P. Eckstein, Kyle J. Myers
Human observer templates for lesion discrimination tasks. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Julia Guillou, Romain Bourcier, Florent Autrusseau
Brain vasculature segmentation based on human perception criteria. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Amy Van Dusen, Michael Vieceli, Karen Drukker, Hiroyuki Abe, Maryellen L. Giger, Heather M. Whitney
Repeatability profiles towards consistent sensitivity and specificity levels for machine learning on breast DCE-MRI. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Matthew A. Kupinski, Zachary Garrett, Jiahua Fan
Observer-driven texture analysis in CT imaging. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Manish Sharma, J. Michael O'Connor, Anitha Singareddy, Madhuri Madasu, Nicholas Enus, Kassel Fotinos-Hoyer, Yibin Shao
Using "Reader Disagreement Index" as a predictive reviewer performance monitoring tool for timely intervention. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Craig K. Abbey, Michael A. Webster, Tanya D. Geertse, Danielle van der Waal, Eric Tetteroo, Ruud Pijnappel, Mireille J. M. Broeders, Ioannis Sechopoulos
Sequential reading effects in Dutch screening mammography. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Alexander Dabrowiecki, Alexander Villalobos, Elizabeth A. Krupinski
Blue light filtering glasses and computer vision syndrome: a pilot study. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Jennie Crosby, Sophia Chen, Feng Li, Heber MacMahon, Maryellen L. Giger
Network output visualization to uncover limitations of deep learning detection of pneumothorax. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Changwoo Lee, Jongduk Baek
Implementation of an anthropomorphic model observer using convolutional neural network for breast tomosynthesis images. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Wei Zhou, Yi Tian, Zhengyu Fei, Yougu Yang
Human visual property based grayscale contrast enhancement: phantom-based effectiveness assessment. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Stephen L. Hillis
Determining Roe and Metz model parameters for simulating multireader multicase confidence-of-disease rating data based on read-data or conjectured Obuchowski-Rockette parameter estimates. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Fenglei Fan, Sangtae Ahn, Bruno De Man, Kristen A. Wangerin, Scott D. Wollenweber, Craig K. Abbey, Paul E. Kinahan
Deep learning-based model observers that replicate human observers for PET imaging. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Ziba Gandomkar, Ernest U. Ekpo, Sarah J. Lewis, Moayyad E. Suleiman, Kriscia Tapia, Tong Li, Seyedamir Tavakoli Taba, Phuong Dung Trieu, Patrick C. Brennan
Investigating the potential of a gist-sensitive computer-aided detection tool. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Ali N. Avanaki, Kathryn S. Espig, Albert Xthona, D. Brooks, J. Young, Tom Kimpe
Perceptual image quality in digital dermoscopy. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Ramy Mohammed Abdlaty, Lillian Doerwald, Joseph Hayward, Qiyin Fang
Spectral assessment of radiation therapy-induced skin erythema. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Jordan D. Fuhrman, Peter Halloran, Rowena Yip, Artit C. Jirapatnakul, Claudia I. Henschke, David F. Yankelevitz, Maryellen L. Giger
Effect of observer variability and training cases on U-Net segmentation performance. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Elizabeth A. Krupinski, Henry Zhan, Sadaf Sahraian, Elham Beheshtian, Robert E. Morales, David M. Yousem
Where's WALDO: a potential tool for training radiology residents? Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Minah Han, Byeongjoon Kim, Jongduk Baek
A performance comparison of convolutional neural network based anthropomorphic model observer and linear model observer for signal-known statistically detection tasks. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Maryellen L. Giger
Towards understanding perception in the latest era of AI in medical imaging (Conference Presentation). Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Miguel A. Lago, Bruno Barufaldi, Predrag R. Bakic, Craig K. Abbey, Andrew D. A. Maidment, Miguel P. Eckstein
Foveated model observer to predict human search performance on virtual digital breast tomosynthesis phantoms. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Zohaib Amjad Khan, Azeddine Beghdadi, Faouzi Alaya Cheikh, Mounir Kaaniche, Egidijus Pelanis, Rafael Palomar, Åsmund Avdem Fretland, Bjørn Edwin, Ole Jakob Elle
Towards a video quality assessment based framework for enhancement of laparoscopic videos. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Byeongjoon Kim, Minah Han, Jongduk Baek
Convolutional neural network-based anthropomorphic model observer for breast cone-beam CT images. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Iris Lorente, Craig K. Abbey, Jovan G. Brankov
Deep learning based model observer by U-Net. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Areej S. Aloufi, Abdulrahman AlNaeem, Abeer Almousa, Khaled Alzimami, Abdulrahman Alfuraih, Bader Alshahrani, Mohammed Zayed, Taghrid AlMashouq, Saud Alnasser, Khera Aldossari, Mona Alomrani, Iman Alzahrani, Elaine F. Harkness, Susan M. Astley
Breast density in Saudi Arabia: intra and inter reader variability in screening mammograms assessed visually using BI-RADS and visual analogue scales. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Brian J. Smith, Stephen L. Hillis
Multi-reader multi-case analysis of variance software for diagnostic performance comparison of imaging modalities. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Xiaochen Zhu, Weijie Chen
Simulation of multi-reader multi-case study data with realistic ROC performance characteristics. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Amanda Koh, Dorina Roy, Alastair Gale, Raluca Mihai, Guprit Atwal, Ian O. Ellis, David R. J. Snead, Yan Chen
Understanding digital pathology performance: an eye tracking study. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Gregory Ongie, Emil Y. Sidky, Ingrid S. Reiser, Xiaochuan Pan
Supervised learning of model observers for assessment of CT image reconstruction algorithms. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Aditya Jonnalagadda, Miguel A. Lago, Bruno Barufaldi, Predrag R. Bakic, Craig K. Abbey, Andrew D. A. Maidment, Miguel P. Eckstein
Evaluation of convolutional neural networks for search in 1/f 2.8 filtered noise and digital breast tomosynthesis phantoms. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Qi Gao, Sui Li, Manman Zhu, Danyang Li, Zhaoying Bian, Qingwen Lv, Dong Zeng, Jianhua Ma
Combined global and local information for blind CT image quality assessment via deep learning. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
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)
Kenny H. Cha, Alexej Gossmann, Nicholas Petrick, Berkman Sahiner
Supplementing training with data from a shifted distribution for machine learning classifiers: adding more cases may not always help. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
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)
Dylan H. Shekter, Frank W. Samuelson
Efficiently calculating ROC curves, AUC, and uncertainty from 2AFC studies with finite samples. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Weimin Zhou, Mark A. Anastasio
Markov-Chain Monte Carlo approximation of the Ideal Observer using generative adversarial networks. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)
Dimitar Petrov, Nicholas Marshall, Liesbeth Vancoillie, Lesley Cockmartin, Hilde Bosmans
Deep learning channelized Hotelling observer for multi-vendor DBT system image quality evaluation. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (2020)

volume 11316, 2020

Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment, Houston, TX, USA, February 15-20, 2020 Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 11316 (2020)

2019

volume 10952, 2019

Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, San Diego, California, United States, 16-21 February 2019 Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 10952 (2019)