16 citations · 17 across the 4 of their papers we have counts for
5 papers
Convolutional Neural Network Model Observers Discount Signal-like Anatomical Structures During Search in Virtual Digital Breast Tomosynthesis Phantoms
Aditya Jonnalagadda, Bruno B. Barufaldi, Andrew D. A. Maidment +3
Model observers are computational tools to evaluate and optimize task-based medical image quality. Linear model observers, such as the Channelized Hotelling Observer (CHO), predict…
Greater benefits of deep learning-based computer-aided detection systems for finding small signals in 3D volumetric medical images
Devi Klein, Srijita Karmakar, Aditya Jonnalagadda +2
Purpose: Radiologists are tasked with visually scrutinizing large amounts of data produced by 3D volumetric imaging modalities. Small signals can go unnoticed during the 3d search…
DEMIST: A deep-learning-based task-specific denoising approach for myocardial perfusion SPECT
Md Ashequr Rahman, Zitong Yu, Richard Laforest +3
There is an important need for methods to process myocardial perfusion imaging (MPI) SPECT images acquired at lower radiation dose and/or acquisition time such that the processed i…
Medical Image Quality Metrics for Foveated Model Observers
Miguel A. Lago, Craig K. Abbey, Miguel P. Eckstein
A recently proposed model observer mimics the foveated nature of the human visual system by processing the entire image with varying spatial detail, executing eye movements and scr…
Foveated Model Observers for Visual Search in 3D Medical Images
Miguel A. Lago, Craig K. Abbey, Miguel P. Eckstein
Model observers have a long history of success in predicting human observer performance in clinically-relevant detection tasks. New 3D image modalities provide more signal informat…