AIMC Topic: Deep Learning

Clear Filters Showing 11401 to 11410 of 28423 articles

A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories.

Nature medicine
Pancreatic cancer is an aggressive disease that typically presents late with poor outcomes, indicating a pronounced need for early detection. In this study, we applied artificial intelligence methods to clinical data from 6 million patients (24,000 p...

Deep learning-based methods for classification of microsatellite instability in endometrial cancer from HE-stained pathological images.

Journal of cancer research and clinical oncology
BACKGROUND: Microsatellite instability (MSI) is one of the essential tumor biomarkers for cancer treatment and prognosis. The presence of more significant PD-L1 expression on the surface of tumor cells in endometrial cancer with MSI suggests that MSI...

Saliency Map and Deep Learning in Binary Classification of Brain Tumours.

Sensors (Basel, Switzerland)
The paper was devoted to the application of saliency analysis methods in the performance analysis of deep neural networks used for the binary classification of brain tumours. We have presented the basic issues related to deep learning techniques. A s...

Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes.

European radiology
OBJECTIVES: Using contrast-enhanced computed tomography (CECT) and deep learning technology to develop a deep learning radiomics nomogram (DLRN) to preoperative predict risk status of patients with thymic epithelial tumors (TETs).

In silico prediction of hERG blockers using machine learning and deep learning approaches.

Journal of applied toxicology : JAT
The human ether-à-go-go-related gene (hERG) is associated with drug cardiotoxicity. If the hERG channel is blocked, it will lead to prolonged QT interval and cause sudden death in severe cases. Therefore, it is important to evaluate the hERG-blocking...

Deep learning for asbestos counting.

Journal of hazardous materials
The PCM (phase contrast microscopy) method for asbestos counting needs special sample treatments, hence it is time consuming and rather expensive. As an alternative, we implemented a deep learning procedure on images directly acquired from the untrea...

Medical image super-resolution reconstruction algorithms based on deep learning: A survey.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: With the high-resolution (HR) requirements of medical images in clinical practice, super-resolution (SR) reconstruction algorithms based on low-resolution (LR) medical images have become a research hotspot. This type of meth...

Deep learning enables super-resolution hydrodynamic flooding process modeling under spatiotemporally varying rainstorms.

Water research
Real-time information on flooding extent, severity, and duration is necessary for effective metropolitan flood emergency management. Existing pluvial flood analysis methods are unable to simulate real-time regional flooding processes under spatiotemp...

Comprehensive dose evaluation of a Deep Learning based synthetic Computed Tomography algorithm for pelvic Magnetic Resonance-only radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Magnetic Resonance (MR)-only radiotherapy enables the use of MR without the uncertainty of MR-Computed Tomography (CT) registration. This requires a synthetic CT (sCT) for dose calculations, which can be facilitated by a novel...

Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review.

Computers in biology and medicine
In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. At early stages, CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, ...