Image quality assessment is important to maintain and improve the imaging system
performance, and conducting a human observer study is considered the most desirable …

The purpose of this study is to investigate deep learning model observer (DLMO) using
classification network for various sizes of mass detection tasks in digital breast …

In this study, we aimed to understand the generalizability of a convolutional neural network
(CNN)-based model observer for breast tomosynthesis images with two different (ie, 30 …

Purpose: To develop a deep learning approach for channelization of the Hotelling model
observer (DL-CHO) and apply to the task based image quality evaluation of digital breast …

Background Since human observer studies are resource‐intensive, mathematical model
observers are frequently used to assess task‐based image quality. The most common …

Architectural distortion (AD) is one of the breast abnormal signs in medical imaging and it is
hard to be detected in clinic because of its subtle appearance and similar intensity with …

In this study, we adapted a transformer-based method to localize lesions in digital breast
tomosynthesis (DBT) images. Compared with convolutional neural network-based object …

Model observers (MO) are widely used in medical imaging to act as surrogates of human
observers in task-based image quality evaluation, frequently towards optimization of …

Digital breast tomosynthesis (DBT) is an emerging breast cancer screening and diagnostic
modality that uses quasi-three-dimensional breast images to provide detailed assessments …

We proposed a convolutional neural network (CNN)-based anthropomorphic model
observer to predict human observer detection performance for breast cone-beam CT …