AI Medical Compendium Topic

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Mammography

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End-to-end deep learning framework for printed circuit board manufacturing defect classification.

Scientific reports
We report a complete deep-learning framework using a single-step object detection model in order to quickly and accurately detect and classify the types of manufacturing defects present on Printed Circuit Board (PCBs). We describe the complete model ...

Machine learning for multi-parametric breast MRI: radiomics-based approaches for lesion classification.

Physics in medicine and biology
In the artificial intelligence era, machine learning (ML) techniques have gained more and more importance in the advanced analysis of medical images in several fields of modern medicine. Radiomics extracts a huge number of medical imaging features re...

Visual and quantitative evaluation of microcalcifications in mammograms with deep learning-based super-resolution.

European journal of radiology
PURPOSE: To evaluate visually and quantitatively the performance of a deep-learning-based super-resolution (SR) model for microcalcifications in digital mammography.

Self-Supervised Bi-Channel Transformer Networks for Computer-Aided Diagnosis.

IEEE journal of biomedical and health informatics
Self-supervised learning (SSL) can alleviate the issue of small sample size, which has shown its effectiveness for the computer-aided diagnosis (CAD) models. However, since the conventional SSL methods share the identical backbone in both the pretext...

A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms.

Sensors (Basel, Switzerland)
One of the most promising research areas in the healthcare industry and the scientific community is focusing on the AI-based applications for real medical challenges such as the building of computer-aided diagnosis (CAD) systems for breast cancer. Tr...

Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection.

BioMed research international
Breast cancer is the most common cancer in women, and the breast mass recognition model can effectively assist doctors in clinical diagnosis. However, the scarcity of medical image samples makes the recognition model prone to overfitting. A breast ma...

Possible strategies for use of artificial intelligence in screen-reading of mammograms, based on retrospective data from 122,969 screening examinations.

European radiology
OBJECTIVES: Artificial intelligence (AI) has shown promising results when used on retrospective data from mammographic screening. However, few studies have explored the possible consequences of different strategies for combining AI and radiologists i...

Reliable quality assurance of X-ray mammography scanner by evaluation the standard mammography phantom image using an interpretable deep learning model.

European journal of radiology
OBJECTIVE: Mammography is the initial examination to detect breast cancer symptoms, and quality control of mammography devices is crucial to maintain accurate diagnosis and to safeguard against degradation of performance. The objective of this study ...

Impact of a Deep Learning Model for Predicting Mammographic Breast Density in Routine Clinical Practice.

Journal of the American College of Radiology : JACR
OBJECTIVE: Legislation in 38 states requires patient notification of dense mammographic breast tissue because increased density is a marker of breast cancer risk and can limit mammographic sensitivity. Because radiologist density assessments vary wid...