Background Evaluation of interstitial lung disease (ILD) at CT is a challenging task that requires experience and is subject to substantial interreader variability. Purpose To investigate whether a proposed content-based image retrieval (CBIR) of sim...
Bloodstream infections (BSI) are a main cause of infectious disease morbidity and mortality worldwide. Early prediction of BSI patients at high risk of poor outcomes is important for earlier decision making and effective patient stratification. We de...
BACKGROUND: Efforts to reduce the radiation dose have continued steadily, with new reconstruction techniques. Recently, image denoising algorithms using artificial neural networks, termed deep learning reconstruction (DLR), have been applied to CT im...
BACKGROUND: Making surgery as less aggressive as possible is best for elderly patients with osteoporotic vertebral compression fractures (OVCFs). Recently, we attempted a more precise, minimally invasive, and robot-assisted kyphoplasty in our clinica...
Cancer imaging : the official publication of the International Cancer Imaging Society
Oct 9, 2021
BACKGROUND: The accuracy of estimating microvascular invasion (MVI) preoperatively in hepatocellular carcinoma (HCC) by clinical observers is low. Most recent studies constructed MVI predictive models utilizing radiological and/or radiomics features ...
OBJECTIVES/HYPOTHESIS: To develop a deep-learning-based automatic diagnosis system for identifying nasopharyngeal carcinoma (NPC) from noncancer (inflammation and hyperplasia), using both white light imaging (WLI) and narrow-band imaging (NBI) nasoph...
OBJECTIVES: The present study aimed to evaluate the performance of a Faster Region-based Convolutional Neural Network (R-CNN) algorithm for tooth detection and numbering on periapical images.
The international journal of medical robotics + computer assisted surgery : MRCAS
Oct 8, 2021
BACKGROUND: Oesophagogastric anastomosis is mainly complicated by its tediousness. We hope to modify an oesophagogastric anastomotic technique that simplifies anastomosis.
To compare the diagnostic performances of physicians and a deep convolutional neural network (CNN) predicting malignancy with ultrasonography images of thyroid nodules with atypia of undetermined significance (AUS)/follicular lesion of undetermined s...
Automatic medical image segmentation plays a critical role in scientific research and medical care. Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to...
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