AI Medical Compendium Topic:
Breast Neoplasms

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Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies.

Journal of cancer research and clinical oncology
BACKGROUND: Breast cancer is a major public health concern, and early diagnosis and classification are critical for effective treatment. Machine learning and deep learning techniques have shown great promise in the classification and diagnosis of bre...

Deep Learning Approaches with Digital Mammography for Evaluating Breast Cancer Risk, a Narrative Review.

Tomography (Ann Arbor, Mich.)
Breast cancer remains the leading cause of cancer-related deaths in women worldwide. Current screening regimens and clinical breast cancer risk assessment models use risk factors such as demographics and patient history to guide policy and assess ris...

A deep-learning-based clinical risk stratification for overall survival in adolescent and young adult women with breast cancer.

Journal of cancer research and clinical oncology
OBJECTIVE: The objective of this study is to construct a novel clinical risk stratification for overall survival (OS) prediction in adolescent and young adult (AYA) women with breast cancer.

Deep Learning Model Based on Dual-Modal Ultrasound and Molecular Data for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer.

Academic radiology
RATIONALE AND OBJECTIVES: To carry out radiomics analysis/deep convolutional neural network (CNN) based on B-mode ultrasound (BUS) and shear wave elastography (SWE) to predict response to neoadjuvant chemotherapy (NAC) in breast cancer patients.

Screening outcome for interpretation by the first and second reader in a population-based mammographic screening program with independent double reading.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: Double reading of screening mammograms is associated with a higher rate of screen-detected cancer than single reading, but different strategies exist regarding reader pairing and blinding. Knowledge about these aspects is important when c...

Differentiation between Phyllodes Tumors and Fibroadenomas through Breast Ultrasound: Deep-Learning Model Outperforms Ultrasound Physicians.

Sensors (Basel, Switzerland)
The preoperative differentiation of breast phyllodes tumors (PTs) from fibroadenomas (FAs) plays a critical role in identifying an appropriate surgical treatment. Although several imaging modalities are available, reliable differentiation between PT ...

A divide and conquer approach to maximise deep learning mammography classification accuracies.

PloS one
Breast cancer claims 11,400 lives on average every year in the UK, making it one of the deadliest diseases. Mammography is the gold standard for detecting early signs of breast cancer, which can help cure the disease during its early stages. However,...

Deep Learning-Based Computer-Aided Diagnosis for Breast Lesion Classification on Ultrasound: A Prospective Multicenter Study of Radiologists Without Breast Ultrasound Expertise.

AJR. American journal of roentgenology
Computer-aided diagnosis (CAD) systems for breast ultrasound interpretation have been primarily evaluated at tertiary and/or urban medical centers by radiologists with breast ultrasound expertise. The purpose of this study was to evaluate the usefu...

Smart IoT in Breast Cancer Detection Using Optimal Deep Learning.

Journal of digital imaging
IoT in healthcare systems is currently a viable option for providing higher-quality medical care for contemporary e-healthcare. Using an Internet of Things (IoT)-based smart healthcare system, a trustworthy breast cancer classification method called ...

Standalone AI for Breast Cancer Detection at Screening Digital Mammography and Digital Breast Tomosynthesis: A Systematic Review and Meta-Analysis.

Radiology
Background There is considerable interest in the potential use of artificial intelligence (AI) systems in mammographic screening. However, it is essential to critically evaluate the performance of AI before it can become a modality used for independe...