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Deep Learning for Contrast Enhanced Mammography - A Systematic Review.
Acad Radiol
December 2024
Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY (B.S.G., G.N.N., E.K.); The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY (G.N.N., E.K.).
Article Synopsis
Diversity, inclusivity and traceability of mammography datasets used in development of Artificial Intelligence technologies: a systematic review.
Clin Imaging
February 2025
University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK; National Institute for Health and Care Research Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK; Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK. Electronic address:
Article Synopsis
- This review analyzes various mammography datasets used for AI development in breast cancer screening, focusing on their transparency, content, and accessibility.
- A search identified 254 datasets, with only 28 being accessible; most datasets came from Europe, East Asia, and North America, raising concerns over poor demographic representation.
- The findings highlight significant gaps in diversity within these datasets, underscoring the need for better documentation and inclusivity to enhance the effectiveness of AI technologies in breast cancer research.
Deep learning empowered breast cancer diagnosis: Advancements in detection and classification.
PLoS One
July 2024
Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.
Recent advancements in AI, driven by big data technologies, have reshaped various industries, with a strong focus on data-driven approaches. This has resulted in remarkable progress in fields like computer vision, e-commerce, cybersecurity, and healthcare, primarily fueled by the integration of machine learning and deep learning models. Notably, the intersection of oncology and computer science has given rise to Computer-Aided Diagnosis (CAD) systems, offering vital tools to aid medical professionals in tumor detection, classification, recurrence tracking, and prognosis prediction.
View Article and Find Full Text PDFPersonalized Breast Cancer Screening.
JCO Clin Cancer Inform
September 2023
Artificial Intelligence and Data Science, Benefits Science Technologies, Boston, MA.
Purpose: Abundant literature and clinical trials indicate that routine cancer screenings decrease patient mortality for several common cancers. However, current national cancer screening guidelines heavily rely on patient age as the predominant factor in deciding cancer screening timing, neglecting other important medical characteristics of individual patients. This approach either delays screening or prescribes excessive screenings.
View Article and Find Full Text PDFData Driven for Early Breast Cancer Staging using Integrated Mammography and Biopsy.
Asian Pac J Cancer Prev
December 2021
Department of Computer Science, Faculty of Business Administration and Information Technology, Rajamangala University of Technology Tawan-Ok, Thailand.
Objective: Breast cancer patients who have a rapid diagnosis have been better prognosis than late diagnosis. The popular screening is mammogram or ultrasound. In recent years, researchers try to develop data driven models to predict early cancer staging from the first screening.
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