AIMC Topic: Mammography
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Diversity, inclusivity and traceability of mammography datasets used in development of Artificial Intelligence technologies: a systematic review.
Clinical imaging
Nov 26, 2024
PURPOSE: There are many radiological datasets for breast cancer, some which have supported the development of AI medical devices for breast cancer screening and image classification. This review aims to identify mammography datasets (including digiti...
Multi-scale region selection network in deep features for full-field mammogram classification.
Medical image analysis
Nov 26, 2024
Early diagnosis and treatment of breast cancer can effectively reduce mortality. Since mammogram is one of the most commonly used methods in the early diagnosis of breast cancer, the classification of mammogram images is an important work of computer...
Enhanced breast mass segmentation in mammograms using a hybrid transformer UNet model.
Computers in biology and medicine
Nov 19, 2024
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
Nov 18, 2024
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
Nov 17, 2024
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
Nov 9, 2024
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
Nov 3, 2024
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
Nov 1, 2024
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...
Evaluation of a deep learning-based software to automatically detect and quantify breast arterial calcifications on digital mammogram.
Diagnostic and interventional imaging
Oct 26, 2024
PURPOSE: The purpose of this study was to evaluate an artificial intelligence (AI) software that automatically detects and quantifies breast arterial calcifications (BAC).
Utilizing Pseudo Color Image to Improve the Performance of Deep Transfer Learning-Based Computer-Aided Diagnosis Schemes in Breast Mass Classification.
Journal of imaging informatics in medicine
Oct 25, 2024
The purpose of this study is to investigate the impact of using morphological information in classifying suspicious breast lesions. The widespread use of deep transfer learning can significantly improve the performance of the mammogram based CADx sch...
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