AIMC Topic: Machine Learning

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A mixed-scale dense convolutional neural network for image analysis.

Proceedings of the National Academy of Sciences of the United States of America
Deep convolutional neural networks have been successfully applied to many image-processing problems in recent works. Popular network architectures often add additional operations and connections to the standard architecture to enable training deeper ...

[Advantages and Application Prospects of Deep Learning in Image Recognition and Bone Age Assessment].

Fa yi xue za zhi
Deep learning and neural network models have been new research directions and hot issues in the fields of machine learning and artificial intelligence in recent years. Deep learning has made a breakthrough in the applications of image and speech reco...

A test of the adaptive network explanation of functional disorders using a machine learning analysis of symptoms.

Bio Systems
The classification and etiology of functional disorders is controversial. Evidence supports both psychological and biological (disease) models that show, respectively, that functional disorders should be classified as one (bodily distress syndrome) a...

Automatic mining of symptom severity from psychiatric evaluation notes.

International journal of methods in psychiatric research
OBJECTIVES: As electronic mental health records become more widely available, several approaches have been suggested to automatically extract information from free-text narrative aiming to support epidemiological research and clinical decision-making...

A deep learning method for classifying mammographic breast density categories.

Medical physics
PURPOSE: Mammographic breast density is an established risk marker for breast cancer and is visually assessed by radiologists in routine mammogram image reading, using four qualitative Breast Imaging and Reporting Data System (BI-RADS) breast density...

Exhaled breath condensate metabolome clusters for endotype discovery in asthma.

Journal of translational medicine
BACKGROUND: Asthma is a complex, heterogeneous disorder with similar presenting symptoms but with varying underlying pathologies. Exhaled breath condensate (EBC) is a relatively unexplored matrix which reflects the signatures of respiratory epitheliu...

An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection.

Sensors (Basel, Switzerland)
The fixed-size non-overlapping sliding window (FNSW) and fixed-size overlapping sliding window (FOSW) approaches are the most commonly used data-segmentation techniques in machine learning-based fall detection using accelerometer sensors. However, th...

Can we accurately classify schizophrenia patients from healthy controls using magnetic resonance imaging and machine learning? A multi-method and multi-dataset study.

Schizophrenia research
Machine learning is a powerful tool that has previously been used to classify schizophrenia (SZ) patients from healthy controls (HC) using magnetic resonance images. Each study, however, uses different datasets, classification algorithms, and validat...

Subtyping cognitive profiles in Autism Spectrum Disorder using a Functional Random Forest algorithm.

NeuroImage
DSM-5 Autism Spectrum Disorder (ASD) comprises a set of neurodevelopmental disorders characterized by deficits in social communication and interaction and repetitive behaviors or restricted interests, and may both affect and be affected by multiple c...