AI Medical Compendium Topic

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Mammography

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Employing Atrous Pyramid Convolutional Deep Learning Approach for Detection to Diagnose Breast Cancer Tumors.

Computational intelligence and neuroscience
Breast cancer is among the most common diseases and one of the most common causes of death in the female population worldwide. Early identification of breast cancer improves survival. Therefore, radiologists will be able to make more accurate diagnos...

Performance of artificial intelligence in 7533 consecutive prevalent screening mammograms from the BreastScreen Australia program.

European radiology
OBJECTIVES: To assess the performance of an artificial intelligence (AI) algorithm in the Australian mammography screening program which routinely uses two independent readers with arbitration of discordant results.

Population-wide evaluation of artificial intelligence and radiologist assessment of screening mammograms.

European radiology
OBJECTIVES: To validate an AI system for standalone breast cancer detection on an entire screening population in comparison to first-reading breast radiologists.

Artificial Intelligence-Driven Mammography-Based Future Breast Cancer Risk Prediction: A Systematic Review.

Journal of the American College of Radiology : JACR
PURPOSE: To summarize the literature regarding the performance of mammography-image based artificial intelligence (AI) algorithms, with and without additional clinical data, for future breast cancer risk prediction.

Deep learning performance for detection and classification of microcalcifications on mammography.

European radiology experimental
BACKGROUND: Breast cancer screening through mammography is crucial for early detection, yet the demand for mammography services surpasses the capacity of radiologists. Artificial intelligence (AI) can assist in evaluating microcalcifications on mammo...

Deep learning, radiomics and radiogenomics applications in the digital breast tomosynthesis: a systematic review.

BMC bioinformatics
BACKGROUND: Recent advancements in computing power and state-of-the-art algorithms have helped in more accessible and accurate diagnosis of numerous diseases. In addition, the development of de novo areas in imaging science, such as radiomics and rad...

Multitask deep learning on mammography to predict extensive intraductal component in invasive breast cancer.

European radiology
OBJECTIVES: To develop a multitask deep learning (DL) algorithm to automatically classify mammography imaging findings and predict the existence of extensive intraductal component (EIC) in invasive breast cancer.

Recent advancements in machine learning and deep learning-based breast cancer detection using mammograms.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
OBJECTIVE: Mammogram-based automatic breast cancer detection has a primary role in accurate cancer diagnosis and treatment planning to save valuable lives. Mammography is one basic yet efficient test for screening breast cancer. Very few comprehensiv...

Automatic classification and prioritisation of actionable BI-RADS categories using natural language processing models.

Clinical radiology
AIM: To facilitate the routine tasks performed by radiologists in their evaluation of breast radiology reports, by enhancing the communication of relevant results to referring physicians via a natural language processing (NLP)-based system to classif...