AIMC Topic: Breast Density
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Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm.
Physics in medicine and biology
Jan 30, 2018
In order to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, this study aims to investigate advantages of applying a machine learning approach embedded with a locally ...
Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning.
Physics in medicine and biology
Jan 9, 2018
Breast density is one of the most significant factors that is associated with cancer risk. In this study, our purpose was to develop a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammograms (DMs). ...
Latent feature representation with depth directional long-term recurrent learning for breast masses in digital breast tomosynthesis.
Physics in medicine and biology
Jan 12, 2017
Characterization of masses in computer-aided detection systems for digital breast tomosynthesis (DBT) is an important step to reduce false positive (FP) rates. To effectively differentiate masses from FPs in DBT, discriminative mass feature represent...
Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring.
IEEE transactions on medical imaging
Feb 18, 2016
Mammographic risk scoring has commonly been automated by extracting a set of handcrafted features from mammograms, and relating the responses directly or indirectly to breast cancer risk. We present a method that learns a feature hierarchy from unlab...
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.
BMJ health & care informatics
May 14, 2025
OBJECTIVES: Evaluate an Artificial Intelligence (AI) system in breast screening through stratified results across age, breast density, ethnicity and screening centres, from different UK regions.
Automated Breast Density Assessment for Full-Field Digital Mammography and Digital Breast Tomosynthesis.
Cancer prevention research (Philadelphia, Pa.)
Jan 6, 2025
Mammographic density is a strong risk factor for breast cancer and is reported clinically as part of Breast Imaging Reporting and Data System (BI-RADS) results issued by radiologists. Automated assessment of density is needed that can be used for bot...
Radiomic analysis of cohort-specific diagnostic errors in reading dense mammograms using artificial intelligence.
The British journal of radiology
Jan 1, 2025
OBJECTIVES: This study aims to investigate radiologists' interpretation errors when reading dense screening mammograms using a radiomics-based artificial intelligence approach.
Screening Outcomes of Mammography with AI in Dense Breasts: A Comparative Study with Supplemental Screening US.
Radiology
Jul 1, 2024
Background Comparative performance between artificial intelligence (AI) and breast US for women with dense breasts undergoing screening mammography remains unclear. Purpose To compare the performance of mammography alone, mammography with AI, and mam...
Effects of vitamin D supplementation on a deep learning-based mammographic evaluation in SWOG S0812.
JNCI cancer spectrum
Jul 1, 2024
Deep learning-based mammographic evaluations could noninvasively assess response to breast cancer chemoprevention. We evaluated change in a convolutional neural network-based breast cancer risk model applied to mammograms among women enrolled in SWOG...
Patient Characteristics Impact Performance of AI Algorithm in Interpreting Negative Screening Digital Breast Tomosynthesis Studies.
Radiology
May 1, 2024
Background Artificial intelligence (AI) is increasingly used to manage radiologists' workloads. The impact of patient characteristics on AI performance has not been well studied. Purpose To understand the impact of patient characteristics (race and e...
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