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Ruben E. van Engen

Researcher at Radboud University Nijmegen

Publications -  30
Citations -  493

Ruben E. van Engen is an academic researcher from Radboud University Nijmegen. The author has contributed to research in topics: Digital mammography & Mammography. The author has an hindex of 10, co-authored 30 publications receiving 436 citations. Previous affiliations of Ruben E. van Engen include Radboud University Nijmegen Medical Centre.

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Journal ArticleDOI

Breast Cancer Screening Results 5 Years after Introduction of Digital Mammography in a Population-based Screening Program

TL;DR: With the FFDM-CAD combination, detection performance is at least as good as that with SFM, and the detection of ductal carcinoma in situ and microcalcification clusters improved with FFDM using CAD, while the recall rate increased.
Proceedings ArticleDOI

Evaluation of software for reading images of the CDMAM test object to assess digital mammography systems

TL;DR: Despite some limitations automated reading of CDMAM images can provide a reproducible means of assessing digital mammography systems against European Guidelines and provide a means of predicting average human performance using the automated reading software.
Journal ArticleDOI

Consequences of digital mammography in population-based breast cancer screening: initial changes and long-term impact on referral rates

TL;DR: Dedicated training in digital screening for radiographers and screening radiologists is recommended as a result of initial inexperience with digital screening images implementing FFDM in a population-based breast cancer screening programme may lead to a strong, but temporary increase in referral.
Journal ArticleDOI

Comparison of a flexible versus a rigid breast compression paddle: pain experience, projected breast area, radiation dose and technical image quality

TL;DR: Although FP performed slightly better in the projected breast area, it moved breast tissue from the image area at chest wall side and RP showed better contrast, especially in the retroglandular area, therefore recommending the use of RP for standard MLO and CC views.
Journal ArticleDOI

New reconstruction algorithm for digital breast tomosynthesis: better image quality for humans and computers.

TL;DR: The new algorithm provides DBT volumes with better contrast and image quality, fewer artifacts, and improved visibility of calcifications for human observers, as well as improved detection performance with deep-learning algorithms.