OBJECTIVES: To develop and evaluate a geometric deep-learning network to automatically place seven palatal landmarks on digitized maxillary dental casts.
Journal of bioinformatics and computational biology
Jul 21, 2021
Accurate detection of N6-methyladenine (6mA) sites by biochemical experiments will help to reveal their biological functions, still, these wet experiments are laborious and expensive. Therefore, it is necessary to introduce a powerful computational m...
The lancet. Gastroenterology & hepatology
Jul 21, 2021
BACKGROUND: White light endoscopy is a pivotal first-line tool for the detection of gastric neoplasms. However, gastric neoplasms can be missed during upper gastrointestinal endoscopy due to the subtle nature of these lesions and varying skill among ...
Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming concern in regard to the physic...
This study proposes a deep learning model for cortical bone segmentation in the mandibular condyle head using cone-beam computed tomography (CBCT) and an automated method for measuring cortical thickness with a color display based on the segmentation...
Breast cancer is a heterogeneous disease. To guide proper treatment decisions for each patient, robust prognostic biomarkers, which allow reliable prognosis prediction, are necessary. Gene feature selection based on microarray data is an approach to ...
Interictal epileptiform discharges (IEDs) are an important and widely accepted biomarker used in the diagnosis of epilepsy based on scalp electroencephalography (EEG). Because the visual detection of IEDs has various limitations, including high time ...
In this paper, we propose an automatic cell counting framework for stimulated Raman scattering (SRS) images, which can assist tumor tissue characteristic analysis, cancer diagnosis, and surgery planning processes. SRS microscopy has promoted tumor di...
OBJECTIVES: The purpose of this study was to build a deep learning model to derive labels from neuroradiology reports and assign these to the corresponding examinations, overcoming a bottleneck to computer vision model development.
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