From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment
Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict
patient outcomes, and inform treatment planning. Here, we review recent applications of ML …
patient outcomes, and inform treatment planning. Here, we review recent applications of ML …
Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review
A Gastounioti, S Desai, VS Ahluwalia, EF Conant… - Breast Cancer …, 2022 - Springer
Background Improved breast cancer risk assessment models are needed to enable
personalized screening strategies that achieve better harm-to-benefit ratio based on earlier …
personalized screening strategies that achieve better harm-to-benefit ratio based on earlier …
When doctors and AI interact: on human responsibility for artificial risks
M Verdicchio, A Perin - Philosophy & Technology, 2022 - Springer
A discussion concerning whether to conceive Artificial Intelligence (AI) systems as
responsible moral entities, also known as “artificial moral agents”(AMAs), has been going on …
responsible moral entities, also known as “artificial moral agents”(AMAs), has been going on …
The role of deep learning in advancing breast cancer detection using different imaging modalities: a systematic review
Simple Summary Breast cancer is the most common cancer, which resulted in the death of
700,000 people around the world in 2020. Various imaging modalities have been utilized to …
700,000 people around the world in 2020. Various imaging modalities have been utilized to …
A competition, benchmark, code, and data for using artificial intelligence to detect lesions in digital breast tomosynthesis
Importance An accurate and robust artificial intelligence (AI) algorithm for detecting cancer in
digital breast tomosynthesis (DBT) could significantly improve detection accuracy and …
digital breast tomosynthesis (DBT) could significantly improve detection accuracy and …
Automatic estimation of knee effusion from limited MRI data
Knee effusion is a common comorbidity in osteoarthritis. To quantify the amount of effusion,
semi quantitative assessment scales have been developed that classify fluid levels on an …
semi quantitative assessment scales have been developed that classify fluid levels on an …
Diagnostic accuracy of automated ACR BI-RADS breast density classification using deep convolutional neural networks
R Sexauer, P Hejduk, K Borkowski, C Ruppert… - European …, 2023 - Springer
Objectives High breast density is a well-known risk factor for breast cancer. This study aimed
to develop and adapt two (MLO, CC) deep convolutional neural networks (DCNN) for …
to develop and adapt two (MLO, CC) deep convolutional neural networks (DCNN) for …
Breast density: current knowledge, assessment methods, and clinical implications
JS Chalfant, AC Hoyt - Journal of Breast Imaging, 2022 - academic.oup.com
Breast density is an accepted independent risk factor for the future development of breast
cancer, and greater breast density has the potential to mask malignancies on …
cancer, and greater breast density has the potential to mask malignancies on …
Automatic classification of simulated breast tomosynthesis whole images for the presence of microcalcification clusters using deep CNNs
Microcalcification clusters (MCs) are among the most important biomarkers for breast
cancer, especially in cases of nonpalpable lesions. The vast majority of deep learning …
cancer, especially in cases of nonpalpable lesions. The vast majority of deep learning …
Mammographic breast density: current assessment methods, clinical implications, and future directions
CE Edmonds, SR O'Brien, EF Conant - Seminars in Ultrasound, CT and …, 2023 - Elsevier
Mammographic breast density is widely accepted as an independent risk factor for the
development of breast cancer. In addition, because dense breast tissue may mask breast …
development of breast cancer. In addition, because dense breast tissue may mask breast …