Explainable Precision Medicine in Breast MRI: A Combined Radiomics and Deep Learning Approach for the Classification of Contrast Agent Uptake.

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.

Automatic Detection of Post-Operative Clips in Mammography Using a U-Net Convolutional Neural Network.

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 network (dCNN) may be used to detect surgical clips in follow-up mammograms after BCS.

Methods: 884 mammograms and 517 tomosynthetic images depicting surgical clips and calcifications were manually segmented and classified.

Automatic and standardized quality assurance of digital mammography and tomosynthesis with deep convolutional neural networks.

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 Methods: In this retrospective study, 11,733 mammograms and synthetic 2D reconstructions from tomosynthesis of 4200 patients from two institutions were analyzed by assessing the presence of seven features which impact image quality in regard to breast positioning. Deep learning was applied to train five dCNN models on features detecting the presence of anatomical landmarks and three dCNN models for localization features.

Green composites based on volcanic red algae Cyanidiales, cellulose, and coffee waste biomass modified with magnetic nanoparticles for the removal of methylene blue.

In this paper, green nanocomposites based on biomass and superparamagnetic nanoparticles were synthesized and used as adsorbents to remove methylene blue (MB) from water with magnetic separation. The adsorbents were synthesized through the wet co-precipitation technique, in which iron-oxide nanoparticles coated the cores based on coffee, cellulose, and red volcanic algae waste. The procedure resulted in materials that could be easily separated from aqueous solutions with magnets.

Diagnostic accuracy of automated ACR BI-RADS breast density classification using deep convolutional neural networks.

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 tomosynthesis reconstructions.

Methods: In total, 4605 synthetic 2D images (1665 patients, age: 57 ± 37 years) were labeled according to the ACR (American College of Radiology) density (A-D).