AIMC Topic: Supervised Machine Learning

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Deep semi-supervised learning for automatic segmentation of inferior alveolar nerve using a convolutional neural network.

BMC oral health
BACKGROUND: The inferior alveolar nerve (IAN) innervates and regulates the sensation of the mandibular teeth and lower lip. The position of the IAN should be monitored prior to surgery. Therefore, a study using artificial intelligence (AI) was planne...

The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics.

Reports on progress in physics. Physical Society (Great Britain)
A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within ...

Application of information theoretic feature selection and machine learning methods for the development of genetic risk prediction models.

Scientific reports
In view of the growth of clinical risk prediction models using genetic data, there is an increasing need for studies that use appropriate methods to select the optimum number of features from a large number of genetic variants with a high degree of r...

Deep semi-supervised learning via dynamic anchor graph embedding in latent space.

Neural networks : the official journal of the International Neural Network Society
Recently, deep semi-supervised graph embedding learning has drawn much attention for its appealing performance on the data with a pre-specified graph structure, which could be predefined or empirically constructed based on given data samples. However...

MAGIC: Manifold and Graph Integrative Convolutional Network for Low-Dose CT Reconstruction.

IEEE transactions on medical imaging
Low-dose computed tomography (LDCT) scans, which can effectively alleviate the radiation problem, will degrade the imaging quality. In this paper, we propose a novel LDCT reconstruction network that unrolls the iterative scheme and performs in both i...

Genes and regulatory mechanisms associated with experimentally-induced bovine respiratory disease identified using supervised machine learning methodology.

Scientific reports
Bovine respiratory disease (BRD) is a multifactorial disease involving complex host immune interactions shaped by pathogenic agents and environmental factors. Advancements in RNA sequencing and associated analytical methods are improving our understa...

Supervised Learning Algorithm for Multilayer Spiking Neural Networks with Long-Term Memory Spike Response Model.

Computational intelligence and neuroscience
As a new brain-inspired computational model of artificial neural networks, spiking neural networks transmit and process information via precisely timed spike trains. Constructing efficient learning methods is a significant research field in spiking n...

Improving the Classification Performance of Esophageal Disease on Small Dataset by Semi-supervised Efficient Contrastive Learning.

Journal of medical systems
The classification of esophageal disease based on gastroscopic images is important in the clinical treatment, and is also helpful in providing patients with follow-up treatment plans and preventing lesion deterioration. In recent years, deep learning...

Fish Segmentation in Sonar Images by Mask R-CNN on Feature Maps of Conditional Random Fields.

Sensors (Basel, Switzerland)
Imaging sonar systems are widely used for monitoring fish behavior in turbid or low ambient light waters. For analyzing fish behavior in sonar images, fish segmentation is often required. In this paper, Mask R-CNN is adopted for segmenting fish in so...