The EMory BrEast imaging Dataset (EMBED): A Racially Diverse,                     Granular Dataset of 3.4 Million Screening and Diagnostic Mammographic                     Images

Jeong, Jiwoong; Vey, Brianna L.; Bhimireddy, Ananth Reddy; Kim, Thomas J.; Santos, Thiago; Correa, Ramón; Dutt, Raman; Mošunjac, Marina; Oprea, Gabriela; Smith, Geoffrey H.; Woo, MinJae; McAdams, Christopher R.; Newell, Mary S.; Banerjee, Imon; Gichoya, Judy Wawira; Trivedi, Hari

doi:10.1148/ryai.220047

Cited by 22 publications

(9 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Manually labeling large data cohorts is impractical; hence, NLP tools allow researchers to build data cohorts by automatically extracting outcomes that serve as labels for training deep learning models at scale [ 50 ]. This has the potential to accelerate the discovery of predictive biomarkers for prognosis, therapeutic response, or the risk of adverse events.…”

Section: Discussionmentioning

confidence: 99%

Natural Language Processing for Breast Imaging: A Systematic Review

Diab

Deng

et al. 2023

Diagnostics

View full text Add to dashboard Cite

Natural Language Processing (NLP) has gained prominence in diagnostic radiology, offering a promising tool for improving breast imaging triage, diagnosis, lesion characterization, and treatment management in breast cancer and other breast diseases. This review provides a comprehensive overview of recent advances in NLP for breast imaging, covering the main techniques and applications in this field. Specifically, we discuss various NLP methods used to extract relevant information from clinical notes, radiology reports, and pathology reports and their potential impact on the accuracy and efficiency of breast imaging. In addition, we reviewed the state-of-the-art in NLP-based decision support systems for breast imaging, highlighting the challenges and opportunities of NLP applications for breast imaging in the future. Overall, this review underscores the potential of NLP in enhancing breast imaging care and offers insights for clinicians and researchers interested in this exciting and rapidly evolving field.

show abstract

Section: Discussionmentioning

confidence: 99%

Natural Language Processing for Breast Imaging: A Systematic Review

Diab

Deng

et al. 2023

Diagnostics

View full text Add to dashboard Cite

show abstract

“…FFDM images in raw DICOM format were obtained from five clinical centers in South Korea and database of United States. Five Korean centers include National Cancer Center (NCC), Samsung Seoul Medical Center (SMC), Seoul Asan Medical Center (AMC), Seoul Boramae Medical Center (BMC), and Inje University Ilsan Paik Hospital (IPH), and the Emory Breast Imaging Dataset (EMBED) includes four hospitals mammographic examination data for eight years (Jeong et al, 2023). The images from the Korean Hospitals were acquired during clinical evaluations, thus following a case-control study design; EMBED is a mixture of longitudinal screening data and hospital cases.…”

Section: Data Sourcementioning

confidence: 99%

Artificial-Intelligence Powered Identification of High-Risk Breast Cancer Subgroups Using Mammography: A Multicenter Study Integrating Automated Brightest Density Measures with Deep Learning Metrics

Jeong,

Lee,

Lee

et al. 2024

Preprint

View full text Add to dashboard Cite

Mammography plays a crucial role in breast cancer (BC) risk assessment. Recent breakthroughs show that deep learning (DL) in mammography is expanding from diagnosis to effective risk prediction. Moreover, the brightest mammographic breast density (MBD), termed "cirrocumulus", signifies an authentic risk. Addressing the challenges in quantifying above recent measures, we present MIDAS: a DL-derived system for multi-level MBD and risk feature score (FS). Using >260,000 multicenter images from South Korea and the US, FS consistently outperforms conventional MBD metrics in risk stratification. Only within the high FS, cirrocumulus further enriches assessment, pinpointing "double-higher" subgroup. Their risk profiles are notable: women in double upper-tertile showed OR=10.20 for Koreans and 5.67 for US, and OR=7.09 for scree-detected cases (US only). We also reveals the "black-box" nature of FS that it predominantly captures complex patterns of higher-intensity MBD. Our research enhances the potential of digital mammography in identifying individuals at elevated BC risks.

show abstract

“…This standardization and established methodology for determining and tracking results has facilitated the creation of multiple large data sets which are a prerequisite for the development of high-performing AI algorithms. There are currently multiple large mammography data sets, some of which contain more than 1 million mammograms with associated patient factors and known clinical outcomes [11][12][13][14]. Many of the available data sets come from various sources including different practice locations, practice types, and multiple mammography vendors.…”

Section: Opportunities In Breast Imaging For Ai Applicationsmentioning

confidence: 99%

Artificial Intelligence Applications in Breast Imaging: Current Status and Future Directions

et al. 2023

View full text Add to dashboard Cite

Attempts to use computers to aid in the detection of breast malignancies date back more than 20 years. Despite significant interest and investment, this has historically led to minimal or no significant improvement in performance and outcomes with traditional computer-aided detection. However, recent advances in artificial intelligence and machine learning are now starting to deliver on the promise of improved performance. There are at present more than 20 FDA-approved AI applications for breast imaging, but adoption and utilization are widely variable and low overall. Breast imaging is unique and has aspects that create both opportunities and challenges for AI development and implementation. Breast cancer screening programs worldwide rely on screening mammography to reduce the morbidity and mortality of breast cancer, and many of the most exciting research projects and available AI applications focus on cancer detection for mammography. There are, however, multiple additional potential applications for AI in breast imaging, including decision support, risk assessment, breast density quantitation, workflow and triage, quality evaluation, response to neoadjuvant chemotherapy assessment, and image enhancement. In this review the current status, availability, and future directions of investigation of these applications are discussed, as well as the opportunities and barriers to more widespread utilization.

show abstract

The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.4 Million Screening and Diagnostic Mammographic Images

Cited by 22 publications

References 24 publications

Natural Language Processing for Breast Imaging: A Systematic Review

Natural Language Processing for Breast Imaging: A Systematic Review

Artificial-Intelligence Powered Identification of High-Risk Breast Cancer Subgroups Using Mammography: A Multicenter Study Integrating Automated Brightest Density Measures with Deep Learning Metrics

Artificial Intelligence Applications in Breast Imaging: Current Status and Future Directions

Contact Info

Product

Resources

About