Carlotta Ruppert
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View article: Enhancing breast positioning quality through real-time AI feedback
Enhancing breast positioning quality through real-time AI feedback Open
Objectives Enhance mammography quality to increase cancer detection by implementing continuous AI-driven feedback mechanisms, ensuring reliable, consistent, and high-quality screening by the ‘Perfect’, ‘Good’, ‘Moderate’, and ‘Inadequate’ …
View article: PGMI assessment in mammography: AI software versus human readers
PGMI assessment in mammography: AI software versus human readers Open
AI has promising potential for automated assessment of diagnostic image quality. Faster, more representative and more objective feedback may support radiographers in their quality management processes. Direct transformation of common PGMI …
View article: Automatic Detection of Post-Operative Clips in Mammography Using a U-Net Convolutional Neural Network
Automatic Detection of Post-Operative Clips in Mammography Using a U-Net Convolutional Neural Network Open
Background: After breast conserving surgery (BCS), surgical clips indicate the tumor bed and, thereby, the most probable area for tumor relapse. The aim of this study was to investigate whether a U-Net-based deep convolutional neural netwo…
View article: Explainable Precision Medicine in Breast MRI: A Combined Radiomics and Deep Learning Approach for the Classification of Contrast Agent Uptake
Explainable Precision Medicine in Breast MRI: A Combined Radiomics and Deep Learning Approach for the Classification of Contrast Agent Uptake Open
In DCE-MRI, the degree of contrast uptake in normal fibroglandular tissue, i.e., background parenchymal enhancement (BPE), is a crucial biomarker linked to breast cancer risk and treatment outcome. In accordance with the Breast Imaging Rep…
View article: Automatic and standardized quality assurance of digital mammography and tomosynthesis with deep convolutional neural networks
Automatic and standardized quality assurance of digital mammography and tomosynthesis with deep convolutional neural networks Open
Objectives The aim of this study was to develop and validate a commercially available AI platform for the automatic determination of image quality in mammography and tomosynthesis considering a standardized set of features. Materials and m…
View article: Diagnostic accuracy of automated ACR BI-RADS breast density classification using deep convolutional neural networks
Diagnostic accuracy of automated ACR BI-RADS breast density classification using deep convolutional neural networks Open
Objectives High breast density is a well-known risk factor for breast cancer. This study aimed to develop and adapt two (MLO, CC) deep convolutional neural networks (DCNN) for automatic breast density classification on synthetic 2D tomosyn…
View article: Detection of microcalcifications in photon-counting dedicated breast-CT using a deep convolutional neural network: Proof of principle
Detection of microcalcifications in photon-counting dedicated breast-CT using a deep convolutional neural network: Proof of principle Open
Deep convolutional networks can be used to detect and classify benign and suspicious MC in breast-CT images.
View article: Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network
Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network Open
The purpose of this study was to determine the feasibility of a deep convolutional neural network (dCNN) to accurately detect abnormal axillary lymph nodes on mammograms. In this retrospective study, 107 mammographic images in mediolateral…