Mapping the gene-regulatory networks dysregulated in human disease would allow the design of network-correcting therapies that treat the core disease mechanism. However, small molecules are traditionally screened for their effects on one to several o...
OBJECTIVES: To investigate the value of full-field digital mammography-based deep learning (DL) in predicting malignancy of Breast Imaging Reporting and Data System (BI-RADS) 4 microcalcifications.
PURPOSE: We aimed to develop a deep learning (DL)-based algorithm for automated quantification of aortic valve calcium (AVC) from non-enhanced electrocardiogram-gated cardiac CT scans and compare performance of DL-measured AVC volume and Agatston sco...
OBJECTIVE: This retrospective study evaluated the model from populations with different breast densities and showed the model's performance on malignancy prediction.
This study aimed to assess the diagnostic performance of deep convolutional neural networks (DCNNs) in classifying breast microcalcification in screening mammograms. To this end, 1579 mammographic images were collected retrospectively from patients e...
International journal of environmental research and public health
35206347
Breast cancer is the most common cancer in women worldwide. It is the most frequently diagnosed cancer among women in 140 countries out of 184 reporting countries. Lesions of breast cancer are abnormal areas in the breast tissues. Various types of br...
PURPOSE: To evaluate visually and quantitatively the performance of a deep-learning-based super-resolution (SR) model for microcalcifications in digital mammography.