AIMC Topic: Neural Networks, Computer

Clear Filters Showing 18341 to 18350 of 31376 articles

A tree-based multiclassification of breast tumor histopathology images through deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Worldwide, the burden of cancer is drastically increasing over the past few years. Among all types of cancers in women, breast cancer (BrC) is the main cause of unnatural deaths. For early diagnosis, histopathology (Hp) imaging is a gold standard for...

MutagenPred-GCNNs: A Graph Convolutional Neural Network-Based Classification Model for Mutagenicity Prediction with Data-Driven Molecular Fingerprints.

Interdisciplinary sciences, computational life sciences
An important task in the early stage of drug discovery is the identification of mutagenic compounds. Mutagenicity prediction models that can interpret relationships between toxicological endpoints and compound structures are especially favorable. In ...

Recognition of Abnormal Chest Compression Depth Using One-Dimensional Convolutional Neural Networks.

Sensors (Basel, Switzerland)
When the displacement of an object is evaluated using sensor data, its movement back to the starting point can be used to correct the measurement error of the sensor. In medicine, the movements of chest compressions also involve a reciprocating movem...

Deep learning for early detection of pathological changes in X-ray bone microstructures: case of osteoarthritis.

Scientific reports
Texture features are designed to quantitatively evaluate patterns of spatial distribution of image pixels for purposes of image analysis and interpretation. Unexplained variations in the texture patterns often lead to misinterpretation and undesirabl...

Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients.

Nature communications
The SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of disease severity is a priority. We collect 58 clinical and biological variables, and chest CT scan data, from 1003 coronavirus-infected patients from ...

Artificial intelligence in dermatopathology: Diagnosis, education, and research.

Journal of cutaneous pathology
Artificial intelligence (AI) utilizes computer algorithms to carry out tasks with human-like intelligence. Convolutional neural networks, a type of deep learning AI, can classify basal cell carcinoma, seborrheic keratosis, and conventional nevi, high...

Repeatability of IVIM biomarkers from diffusion-weighted MRI in head and neck: Bayesian probability versus neural network.

Magnetic resonance in medicine
PURPOSE: The intravoxel incoherent motion (IVIM) model for DWI might provide useful biomarkers for disease management in head and neck cancer. This study compared the repeatability of three IVIM fitting methods to the conventional nonlinear least-squ...

Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research.

Experimental neurology
By promising more accurate diagnostics and individual treatment recommendations, deep neural networks and in particular convolutional neural networks have advanced to a powerful tool in medical imaging. Here, we first give an introduction into method...

A robust identification method for nonferrous metal scraps based on deep learning and superpixel optimization.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA
End-of-life vehicles (ELVs) provide a particularly potent source of supply for metals. Hence, the recycling and sorting techniques for ferrous and nonferrous metal scraps from ELVs significantly increase metal resource utilization. However, different...

Using Domain-Specific Fingerprints Generated Through Neural Networks to Enhance Ligand-Based Virtual Screening.

Journal of chemical information and modeling
Similarity-based virtual screening is a fundamental tool in the early drug discovery process and relies heavily on molecular fingerprints. We propose a novel strategy of generating domain-specific fingerprints by training neural networks on target-sp...