• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 41 matches

• 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. Image Perception, Observer Performance, and Technology Assessment 2020: 113160U
• 
  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. Image Perception, Observer Performance, and Technology Assessment 2020: 113160G
• 
  Ramy Mohammed Abdlaty, Lillian Doerwald, Joseph Hayward, Qiyin Fang:
  Spectral assessment of radiation therapy-induced skin erythema. Image Perception, Observer Performance, and Technology Assessment 2020: 1131619
• 
  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. Image Perception, Observer Performance, and Technology Assessment 2020: 113160H
• 
  Abdulaziz S. Alshabibi, Moayyad E. Suleiman, Kriscia A. Tapia, Robert Heard, Patrick C. Brennan:
  Effect of time of day on radiology image interpretations. Image Perception, Observer Performance, and Technology Assessment 2020: 1131608
• 
  Alireza Nasiri Avanaki, Kathryn S. Espig, Albert Xthona, D. Brooks, J. Young, Tom Kimpe:
  Perceptual image quality in digital dermoscopy. Image Perception, Observer Performance, and Technology Assessment 2020: 1131614
• 
  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. Image Perception, Observer Performance, and Technology Assessment 2020: 113160S
• 
  Jennie Crosby, Sophia Chen, Feng Li, Heber MacMahon, Maryellen L. Giger:
  Network output visualization to uncover limitations of deep learning detection of pneumothorax. Image Perception, Observer Performance, and Technology Assessment 2020: 113160O
• 
  Alexander Dabrowiecki, Alexander Villalobos, Elizabeth A. Krupinski:
  Blue light filtering glasses and computer vision syndrome: a pilot study. Image Perception, Observer Performance, and Technology Assessment 2020: 1131609
• 
  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. Image Perception, Observer Performance, and Technology Assessment 2020: 113160I
• 
  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. Image Perception, Observer Performance, and Technology Assessment 2020: 113160E
• 
  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. Image Perception, Observer Performance, and Technology Assessment 2020: 113160T
• 
  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. Image Perception, Observer Performance, and Technology Assessment 2020: 113160J
• 
  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. Image Perception, Observer Performance, and Technology Assessment 2020: 1131615
• 
  Maryellen L. Giger:
  Towards understanding perception in the latest era of AI in medical imaging (Conference Presentation). Image Perception, Observer Performance, and Technology Assessment 2020: 1131602
• 
  Jason L. Granstedt, Weimin Zhou, Mark A. Anastasio:
  Learning efficient channels with a dual loss autoencoder. Image Perception, Observer Performance, and Technology Assessment 2020: 113160C
• 
  Julia Guillou, Romain Bourcier, Florent Autrusseau:
  Brain vasculature segmentation based on human perception criteria. Image Perception, Observer Performance, and Technology Assessment 2020: 113160Z
• 
  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. Image Perception, Observer Performance, and Technology Assessment 2020: 1131612
• 
  Shenghua He, Weimin Zhou, Hua Li, Mark A. Anastasio:
  Learning numerical observers using unsupervised domain adaptation. Image Perception, Observer Performance, and Technology Assessment 2020: 113160W
• 
  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. Image Perception, Observer Performance, and Technology Assessment 2020: 113160N
• 
  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. Image Perception, Observer Performance, and Technology Assessment 2020: 1131617
• 
  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. Image Perception, Observer Performance, and Technology Assessment 2020: 113160P
• 
  Byeongjoon Kim, Minah Han, Jongduk Baek:
  Convolutional neural network-based anthropomorphic model observer for breast cone-beam CT images. Image Perception, Observer Performance, and Technology Assessment 2020: 113160Y
• 
  Amanda Koh, Dorina Roy, Alastair G. Gale, Raluca Mihai, Guprit Atwal, Ian O. Ellis, David R. J. Snead, Yan Chen:
  Understanding digital pathology performance: an eye tracking study. Image Perception, Observer Performance, and Technology Assessment 2020: 1131607
• 
  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? Image Perception, Observer Performance, and Technology Assessment 2020: 1131605
• 
  Matthew A. Kupinski, Zachary Garrett, Jiahua Fan:
  Observer-driven texture analysis in CT imaging. Image Perception, Observer Performance, and Technology Assessment 2020: 1131610
• 
  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. Image Perception, Observer Performance, and Technology Assessment 2020: 113160V
• 
  Changwoo Lee, Jongduk Baek:
  Implementation of an anthropomorphic model observer using convolutional neural network for breast tomosynthesis images. Image Perception, Observer Performance, and Technology Assessment 2020: 1131611
• 
  Iris Lorente, Craig K. Abbey, Jovan G. Brankov:
  Deep learning based model observer by U-Net. Image Perception, Observer Performance, and Technology Assessment 2020: 113160F
• 
  Gregory Ongie, Emil Y. Sidky, Ingrid S. Reiser, Xiaochuan Pan:
  Supervised learning of model observers for assessment of CT image reconstruction algorithms. Image Perception, Observer Performance, and Technology Assessment 2020: 113160B

skipping 11 more matches