نتائج البحث

Area-based breast percentage density estimation in mammograms using weight-adaptive multitask learning.

• Authors : Gudhe NR; Institute of Clinical Medicine, Pathology and Forensic Medicine, Multidisciplinary Cancer Research community, University of Eastern Finland, P.O. Box 1627, 70211, Kuopio, Finland. .; Behravan H

Subjects: Breast Density* ; Breast Neoplasms*/Breast Neoplasms*/Breast Neoplasms*/diagnostic imaging; Breast/Breast/Breast/diagnostic imaging

• Source: Scientific reports [Sci Rep] 2022 Jul 14; Vol. 12 (1), pp. 12060. Date of Electronic Publication: 2022 Jul 14.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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Classification of normal screening mammograms is strongly influenced by perceived mammographic breast density.

• Authors : Ang ZZ; Medical Imaging Optimisation and Perception Group (MIOPeG), Faculty of Health Sciences, Discipline of Medical Radiation Sciences, The University of Sydney, Lidcombe, New South Wales, Australia.; National Healthcare Group Diagnostics (NHGD), Singapore City, Singapore.

Subjects: Breast Density*; Breast Neoplasms/Breast Neoplasms/Breast Neoplasms/*diagnostic imaging; Clinical Competence

• Source: Journal of medical imaging and radiation oncology [J Med Imaging Radiat Oncol] 2017 Aug; Vol. 61 (4), pp. 461-469. Date of Electronic Publication: 2017 Jan 03.Publisher: Wiley-Blackwell Pub. Asia Country of Publication: Australia NLM ID: 101469340 Publication Model: Print-Electronic Cited Medium: Internet

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Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study

• Authors : Hinton, Benjamin; Ma, Lin; Mahmoudzadeh, Amir Pasha

Subjects: Biomedical and Clinical Sciences; Oncology and Carcinogenesis; Women's Health

• Source: Cancer Imaging. 19(1)

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Improving radiologist's ability in identifying particular abnormal lesions on mammograms through training test set with immediate feedback

• Authors : Trieu, P. D.; Lewis, Sarah (R21243); Li, T.

Subjects: Adult; Science; Breast Neoplasms

• Source: Sci RepScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)

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Mass segmentation of mammograms using Markov models associated with constrained clustering

• Authors : Raúl Cruz-Barbosa; Saiveth Hernández-Hernández; Luis Enrique Sucar

Subjects: Databases, Factual; Breast Neoplasms; 02 engineering and technology

• Source: Medical & Biological Engineering & Computing. 58:2475-2495

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Machine Learning-Based Approaches for Breast Density Estimation from Mammograms: A Comprehensive Review

• Authors : Khaldoon Alhusari; Salam Dhou

Subjects: breast cancer; breast density; mammographic density estimation

• Source: Journal of Imaging ; Volume 11 ; Issue 2 ; Pages: 38

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Assessing artificial intelligence in breast screening with stratified results on 306 839 mammograms across geographic regions, age, breast density and ethnicity: A Retrospective Investigation Evaluating Screening (ARIES) study

• Authors : Oberije, C. J. G.; Currie, R.; Leaver, A. ...

Subjects: Humans; Retrospective Studies; Female

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Association between Computed Tissue Density Asymmetry in Bilateral Mammograms and Near-term Breast Cancer Risk

• Authors : David Gur; Maxine Tan; Pandiyarajan Ramalingam

Subjects: Adult; Breast Neoplasms; Middle Aged

• Source: The Breast Journal. 20:249-257

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An Approach to Classifying Breast Density Level Using Deep Learning-Based Segmentation Model on Full-Field Digital Mammograms

• Authors : Ozic, M.U.; Yilmaz, A.S.; Sandiraz, H.I.

Subjects: Breast Density; Classification; Deep Learning

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Local Septenary Patterns for Breast Density Classification in Mammograms

• Authors : Rampun, Andrik; Scotney, Bryan; Wang, Hui

Subjects: Breast Density; Mammograms; Local Septenary Patterns

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