AIMC Topic: Machine Learning

Clear Filters Showing 15361 to 15370 of 34417 articles

Novel neural network model for predicting susceptibility of facial post-inflammatory hyperpigmentation.

Medical engineering & physics
BACKGROUND: To construct a neural network model (ATBP) for predicting susceptibility to Post-inflammatory hyperpigmentation (PIH), which is a rapid, objective, and reliable decision-support method before physical and chemical interventions in dermato...

Machine learning in neuroimaging: from research to clinical practice.

Radiologie (Heidelberg, Germany)
Neuroimaging is critical in clinical care and research, enabling us to investigate the brain in health and disease. There is a complex link between the brain's morphological structure, physiological architecture, and the corresponding imaging charact...

Printability for additive manufacturing with machine learning: Hybrid intelligent Gaussian process surrogate-based neural network model for Co-Cr alloy.

Journal of the mechanical behavior of biomedical materials
AM has revolutionized the manufacturing industry, involving several operating parameters that may affect the properties of the final manufactured part. In AM, LPBF has proved its reliability in producing dense components; however, process development...

Breast cancer detection using deep learning: Datasets, methods, and challenges ahead.

Computers in biology and medicine
Breast Cancer (BC) is the most commonly diagnosed cancer and second leading cause of mortality among women. About 1 in 8 US women (about 13%) will develop invasive BC throughout their lifetime. Early detection of this life-threatening disease not onl...

Artificial intelligence in multiparametric magnetic resonance imaging: A review.

Medical physics
Multiparametric magnetic resonance imaging (mpMRI) is an indispensable tool in the clinical workflow for the diagnosis and treatment planning of various diseases. Machine learning-based artificial intelligence (AI) methods, especially those adopting ...

SMICLR: Contrastive Learning on Multiple Molecular Representations for Semisupervised and Unsupervised Representation Learning.

Journal of chemical information and modeling
Machine learning as a tool for chemical space exploration broadens horizons to work with known and unknown molecules. At its core lies molecular representation, an essential key to improve learning about structure-property relationships. Recently, co...

Multi-Modal Graph Learning for Disease Prediction.

IEEE transactions on medical imaging
Benefiting from the powerful expressive capability of graphs, graph-based approaches have been popularly applied to handle multi-modal medical data and achieved impressive performance in various biomedical applications. For disease prediction tasks, ...

BNAS: Efficient Neural Architecture Search Using Broad Scalable Architecture.

IEEE transactions on neural networks and learning systems
Efficient neural architecture search (ENAS) achieves novel efficiency for learning architecture with high-performance via parameter sharing and reinforcement learning (RL). In the phase of architecture search, ENAS employs deep scalable architecture ...

Local Critic Training for Model-Parallel Learning of Deep Neural Networks.

IEEE transactions on neural networks and learning systems
In this article, we propose a novel model-parallel learning method, called local critic training, which trains neural networks using additional modules called local critic networks. The main network is divided into several layer groups, and each laye...