AI Medical Compendium Topic

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Mammography

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Enhanced breast mass segmentation in mammograms using a hybrid transformer UNet model.

Computers in biology and medicine
Breast mass segmentation plays a crucial role in early breast cancer detection and diagnosis, and while Convolutional Neural Networks (CNN) have been widely used for this task, their reliance on local receptive fields limits ability to capture long-r...

Technical feasibility of automated blur detection in digital mammography using convolutional neural network.

European radiology experimental
BACKGROUND: The presence of a blurred area, depending on its localization, in a mammogram can limit diagnostic accuracy. The goal of this study was to develop a model for automatic detection of blur in diagnostically relevant locations in digital mam...

Multi-Institutional Evaluation and Training of Breast Density Classification AI Algorithm Using ACR Connect and AI-LAB.

Journal of the American College of Radiology : JACR
OBJECTIVE: To demonstrate and test the capabilities of the ACR Connect and AI-LAB software platform by implementing multi-institutional artificial intelligence (AI) training and validation for breast density classification.

An open codebase for enhancing transparency in deep learning-based breast cancer diagnosis utilizing CBIS-DDSM data.

Scientific reports
Accessible mammography datasets and innovative machine learning techniques are at the forefront of computer-aided breast cancer diagnosis. However, the opacity surrounding private datasets and the unclear methodology behind the selection of subset im...

Combination of Deep Learning Grad-CAM and Radiomics for Automatic Localization and Diagnosis of Architectural Distortion on DBT.

Academic radiology
RATIONALE AND OBJECTIVES: Detection and diagnosis of architectural distortion (AD) on digital breast tomosynthesis (DBT) is challenging. This study applied artificial intelligence (AI) using deep learning (DL) algorithms to detect AD, followed by rad...

Integrative hybrid deep learning for enhanced breast cancer diagnosis: leveraging the Wisconsin Breast Cancer Database and the CBIS-DDSM dataset.

Scientific reports
The objective of this investigation was to improve the diagnosis of breast cancer by combining two significant datasets: the Wisconsin Breast Cancer Database and the DDSM Curated Breast Imaging Subset (CBIS-DDSM). The Wisconsin Breast Cancer Database...

Increasing transparency of computer-aided detection impairs decision-making in visual search.

Psychonomic bulletin & review
Recent developments in artificial intelligence (AI) have led to changes in healthcare. Government and regulatory bodies have advocated the need for transparency in AI systems with recommendations to provide users with more details about AI accuracy a...

Deep-AutoMO: Deep automated multiobjective neural network for trustworthy lesion malignancy diagnosis in the early stage via digital breast tomosynthesis.

Computers in biology and medicine
Breast cancer is the most prevalent cancer in women, and early diagnosis of malignant lesions is crucial for developing treatment plans. Digital breast tomosynthesis (DBT) has emerged as a valuable tool for early breast cancer detection, as it can id...

A systematic scoping review exploring variation in practice in specimen mammography for Intraoperative Margin Analysis in Breast Conserving Surgery and the role of artificial intelligence in optimising diagnostic accuracy.

European journal of radiology
PURPOSE: Specimen Mammography (SM) is commonly used in Breast Conserving Surgery (BCS) for intraoperative margin analysis. A systematic scoping review was conducted to identify sources of methodological variation in Specimen Mammography Interpretatio...