AIMC Topic: Algorithms

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seqQscorer: automated quality control of next-generation sequencing data using machine learning.

Genome biology
Controlling quality of next-generation sequencing (NGS) data files is a necessary but complex task. To address this problem, we statistically characterize common NGS quality features and develop a novel quality control procedure involving tree-based ...

A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers.

Journal of neural engineering
Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or physical status of the individuals by signal decoding. The emerging deep learning technique...

Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
The vast amount of data produced by today's medical imaging systems has led medical professionals to turn to novel technologies in order to efficiently handle their data and exploit the rich information present in them. In this context, artificial in...

Machine Learning Model for Computational Tracking and Forecasting the COVID-19 Dynamic Propagation.

IEEE journal of biomedical and health informatics
A computational model with intelligent machine learning for analysis of epidemiological data, is proposed. The innovations of adopted methodology consist of an interval type-2 fuzzy clustering algorithm based on adaptive similarity distance mechanism...

3D Context-Aware Convolutional Neural Network for False Positive Reduction in Clustered Microcalcifications Detection.

IEEE journal of biomedical and health informatics
False positives (FPs) reduction is indispensable for clustered microcalcifications (MCs) detection in digital breast tomosynthesis (DBT), since there might be excessive false candidates in the detection stage. Considering that DBT volume has an aniso...

A Fault Prediction and Cause Identification Approach in Complex Industrial Processes Based on Deep Learning.

Computational intelligence and neuroscience
Faults occurring in the production line can cause many losses. Predicting the fault events before they occur or identifying the causes can effectively reduce such losses. A modern production line can provide enough data to solve the problem. However,...

Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs.

Dento maxillo facial radiology
OBJECTIVE: This study evaluated the use of a deep-learning approach for automated detection and numbering of deciduous teeth in children as depicted on panoramic radiographs.

Discovering Collective Variables of Molecular Transitions via Genetic Algorithms and Neural Networks.

Journal of chemical theory and computation
With the continual improvement of computing hardware and algorithms, simulations have become a powerful tool for understanding all sorts of (bio)molecular processes. To handle the large simulation data sets and to accelerate slow, activated transitio...

Automatic liver segmentation using 3D convolutional neural networks with a hybrid loss function.

Medical physics
PURPOSE: Automatic liver segmentation from abdominal computed tomography (CT) images is a fundamental task in computer-assisted liver surgery programs. Many liver segmentation algorithms are very sensitive to fuzzy boundaries and heterogeneous pathol...

Effects of Image Degradation and Degradation Removal to CNN-Based Image Classification.

IEEE transactions on pattern analysis and machine intelligence
Just like many other topics in computer vision, image classification has achieved significant progress recently by using deep learning neural networks, especially the Convolutional Neural Networks (CNNs). Most of the existing works focused on classif...