AI Medical Compendium Topic

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Mammography

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Hybrid transformer-based model for mammogram classification by integrating prior and current images.

Medical physics
BACKGROUND: Breast cancer screening via mammography plays a crucial role in early detection, significantly impacting women's health outcomes worldwide. However, the manual analysis of mammographic images is time-consuming and requires specialized exp...

Applying YOLOv6 as an ensemble federated learning framework to classify breast cancer pathology images.

Scientific reports
The most common carcinoma-related cause of death among women is breast cancer. Early detection is crucial, and the manual screening method may lead to a delayed diagnosis, which would delay treatment and put lives at risk. Mammography imaging is advi...

Triaging mammography with artificial intelligence: an implementation study.

Breast cancer research and treatment
PURPOSE: Many breast centers are unable to provide immediate results at the time of screening mammography which results in delayed patient care. Implementing artificial intelligence (AI) could identify patients who may have breast cancer and accelera...

A quantum-optimized approach for breast cancer detection using SqueezeNet-SVM.

Scientific reports
Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretatio...

Classifying the molecular subtype of breast cancer using vision transformer and convolutional neural network features.

Breast cancer research and treatment
PURPOSE: Identification of the molecular subtypes in breast cancer allows to optimize treatment strategies, but usually requires invasive needle biopsy. Recently, non-invasive imaging has emerged as promising means to classify them. Magnetic resonanc...

DIFLF: A domain-invariant features learning framework for single-source domain generalization in mammogram classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Single-source domain generalization (SSDG) aims to generalize a deep learning (DL) model trained on one source dataset to multiple unseen datasets. This is important for the clinical applications of DL-based models to breast...

Attention-guided erasing for enhanced transfer learning in breast abnormality classification.

International journal of computer assisted radiology and surgery
PURPOSE: Breast cancer remains one of the most prevalent cancers globally, necessitating effective early screening and diagnosis. This study investigates the effectiveness and generalizability of our recently proposed data augmentation technique, att...

Screening performance and characteristics of breast cancer detected in the Mammography Screening with Artificial Intelligence trial (MASAI): a randomised, controlled, parallel-group, non-inferiority, single-blinded, screening accuracy study.

The Lancet. Digital health
BACKGROUND: Emerging evidence suggests that artificial intelligence (AI) can increase cancer detection in mammography screening while reducing screen-reading workload, but further understanding of the clinical impact is needed.

Using AI to Select Women with Intermediate Breast Cancer Risk for Breast Screening with MRI.

Radiology
Background Combined mammography and MRI screening is not universally accessible for women with intermediate breast cancer risk due to limited MRI resources. Selecting women for MRI by assessing their mammogram may enable more resource-effective scree...