OBJECTIVE: To evaluate the performance of a convolutional neural network (CNN) model that can automatically detect and classify rib fractures, and output structured reports from computed tomography (CT) images.
International journal of gynecological pathology : official journal of the International Society of Gynecological Pathologists
Jul 1, 2020
Benign normal (NL), premalignant (endometrial intraepithelial neoplasia, EIN) and malignant (cancer, EMCA) endometria must be precisely distinguished for optimal management. EIN was objectively defined previously as a regression model incorporating m...
Studies in health technology and informatics
Jun 26, 2020
Analyses of nerve histology are core assays in basic and applied research and even in clinical setting. Detailed report on nerve morphology may unbiased indicate the current state of a peripheral nerve. Manual method requires trained technician and i...
Studies in health technology and informatics
Jun 16, 2020
Analyses of nerve histology are core assays in basic and applied research and even in clinical setting. Detailed report on nerve morphology may unbiasedly indicate the current state of a peripheral nerve. Manual method requires trained technician and...
We developed a fully automated method for brain tumor segmentation using deep learning; 285 brain tumor cases with multiparametric magnetic resonance images from the BraTS2018 data set were used. We designed 3 separate 3D-Dense-UNets to simplify the ...
Recent years have witnessed tremendous growth in the application of machine learning (ML) and deep learning (DL) techniques in medical physics. Embracing the current big data era, medical physicists equipped with these state-of-the-art tools should b...
In recent years, significant progress has been made in developing more accurate and efficient machine learning algorithms for segmentation of medical and natural images. In this review article, we highlight the imperative role of machine learning alg...
Radiomics is an emerging area in quantitative image analysis that aims to relate large-scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning has enabled t...
PURPOSE: The aim of this study was to evaluate random forests (RFs) to identify ROIs on F-florbetapir and F-FDG PET associated with Montreal Cognitive Assessment (MoCA) score.
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.