Results 11 to 20 of about 203,616 (377)

VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography [PDF]

medRxiv, 2022
Mammography, or breast X-ray imaging, is the most widely used imaging modality to detect cancer and other breast diseases. Recent studies have shown that deep learning-based computer-assisted detection and diagnosis (CADe/x) tools have been developed to ...
H. T. Nguyen, H. Q. Nguyen, H. Pham, K. Lam, L. Le, M. Dao, V. Vu   +6 more
semanticscholar   +1 more source

Standalone AI for Breast Cancer Detection at Screening Digital Mammography and Digital Breast Tomosynthesis: A Systematic Review and Meta-Analysis.

Radiology, 2023
Background There is considerable interest in the potential use of artificial intelligence (AI) systems in mammographic screening. However, it is essential to critically evaluate the performance of AI before it can become a modality used for independent ...
J. H. Yoon, Fredrik Strand, P. Baltzer, E. Conant, F. Gilbert, C. Lehman, E. Morris, Lisa A. Mullen, R. Nishikawa, Nitasha Sharma, I. Vejborg, L. Moy, R. Mann   +12 more
semanticscholar   +1 more source

Contrast-enhanced Mammography: State of the Art.

Radiology, 2021
Contrast-enhanced mammography (CEM) has emerged as a viable alternative to contrast-enhanced breast MRI, and it may increase access to vascular imaging while reducing examination cost.
M. Jochelson, M. Lobbes
semanticscholar   +1 more source

Cumulative Probability of False-Positive Results After 10 Years of Screening With Digital Breast Tomosynthesis vs Digital Mammography

JAMA Network Open, 2022
Key Points Question Is there a difference between screening with digital breast tomosynthesis vs digital mammography in the probability of false-positive results after 10 years of screening?
Thao-Quyen H Ho, M. Bissell, K. Kerlikowske, R. Hubbard, B. Sprague, Christoph I. Lee, J. Tice, A. Tosteson, D. Miglioretti   +8 more
semanticscholar   +1 more source

Deep Learning to Improve Breast Cancer Detection on Screening Mammography [PDF]

Scientific Reports, 2017
The rapid development of deep learning, a family of machine learning techniques, has spurred much interest in its application to medical imaging problems.
Li Shen, L. Margolies, J. Rothstein, Eugene Fluder, R. McBride, W. Sieh   +5 more
semanticscholar   +1 more source

Benefits and harms of breast cancer mammography screening for women at average risk of breast cancer: A systematic review for the European Commission Initiative on Breast Cancer

Journal of Medical Screening, 2021
Objectives Mammography screening is generally accepted in women aged 50–69, but the balance between benefits and harms remains controversial in other age groups.
C. Canelo-Aybar, D. S. Ferreira, Monica Ballesteros, M. Posso, N. Montero, I. Solá, Z. Saz-Parkinson, D. Lerda, P. Rossi, S. Duffy, M. Follmann, Axel Graewingholt, P. Alonso-Coello   +12 more
semanticscholar   +1 more source

OPTIMAM Mammography Image Database: a large scale resource of mammography images and clinical data [PDF]

Radiology: Artificial Intelligence, 2020
Supplemental material is available for this article.
M. Halling-Brown, L. Warren, D. Ward, E. Lewis, Alistair Mackenzie, M. Wallis, L. Wilkinson, R. Given-Wilson, R. McAvinchey, K. Young   +9 more
semanticscholar   +1 more source

Evaluation of Adjunctive Ultrasonography for Breast Cancer Detection Among Women Aged 40-49 Years With Varying Breast Density Undergoing Screening Mammography

JAMA Network Open, 2021
Key Points Question Does the performance of adjunctive ultrasonography for breast cancer detection among women undergoing screening mammography change according to breast tissue density? Findings In this secondary analysis of a randomized clinical trial,
Narumi Harada-Shoji, A. Suzuki, T. Ishida, Ying-Fang Zheng, Yoko Narikawa-Shiono, Akiko Sato-Tadano, R. Ohta, N. Ohuchi   +7 more
semanticscholar   +1 more source

Changes in Mammography Utilization by Women’s Characteristics during the First 5 Months of the COVID-19 Pandemic

Journal of the National Cancer Institute, 2021
Background The coronavirus disease 2019 (COVID-19) pandemic led to a near-total cessation of mammography services in the United States in mid-March 2020.
B. Sprague, K. Lowry, D. Miglioretti, Nila H. Alsheik, E. Bowles, A. Tosteson, G. Rauscher, S. Herschorn, Janie M. Lee, A. Trentham-Dietz, D. Weaver, N. Stout, K. Kerlikowske   +12 more
semanticscholar   +1 more source

Deep Learning-Based Artificial Intelligence for Mammography

Korean Journal of Radiology, 2021
During the past decade, researchers have investigated the use of computer-aided mammography interpretation. With the application of deep learning technology, artificial intelligence (AI)-based algorithms for mammography have shown promising results in ...
J. H. Yoon, Eun Kyung Kim
semanticscholar   +1 more source