AIMC Topic: Humans

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Artificial intelligence-based radiotherapy machine parameter optimization using reinforcement learning.

Medical physics
PURPOSE: To develop and evaluate a volumetric modulated arc therapy (VMAT) machine parameter optimization (MPO) approach based on deep-Q reinforcement learning (RL) capable of finding an optimal machine control policy using previous prostate cancer p...

A modeling framework for adaptive lifelong learning with transfer and savings through gating in the prefrontal cortex.

Proceedings of the National Academy of Sciences of the United States of America
The prefrontal cortex encodes and stores numerous, often disparate, schemas and flexibly switches between them. Recent research on artificial neural networks trained by reinforcement learning has made it possible to model fundamental processes underl...

Application of Transfer Learning in EEG Decoding Based on Brain-Computer Interfaces: A Review.

Sensors (Basel, Switzerland)
The algorithms of electroencephalography (EEG) decoding are mainly based on machine learning in current research. One of the main assumptions of machine learning is that training and test data belong to the same feature space and are subject to the s...

Smartphone Motion Sensor-Based Complex Human Activity Identification Using Deep Stacked Autoencoder Algorithm for Enhanced Smart Healthcare System.

Sensors (Basel, Switzerland)
Human motion analysis using a smartphone-embedded accelerometer sensor provided important context for the identification of static, dynamic, and complex sequence of activities. Research in smartphone-based motion analysis are implemented for tasks, s...

Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography.

Scientific reports
Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development an...

Deep learning approach for predicting functional Z-DNA regions using omics data.

Scientific reports
Computational methods to predict Z-DNA regions are in high demand to understand the functional role of Z-DNA. The previous state-of-the-art method Z-Hunt is based on statistical mechanical and energy considerations about B- to Z-DNA transition using ...

Unsupervised and supervised learning with neural network for human transcriptome analysis and cancer diagnosis.

Scientific reports
Deep learning analysis of images and text unfolds new horizons in medicine. However, analysis of transcriptomic data, the cause of biological and pathological changes, is hampered by structural complexity distinctive from images and text. Here we con...

Engineering microrobots for targeted cancer therapies from a medical perspective.

Nature communications
Systemic chemotherapy remains the backbone of many cancer treatments. Due to its untargeted nature and the severe side effects it can cause, numerous nanomedicine approaches have been developed to overcome these issues. However, targeted delivery of ...

Cumulative learning enables convolutional neural network representations for small mass spectrometry data classification.

Nature communications
Rapid and accurate clinical diagnosis remains challenging. A component of diagnosis tool development is the design of effective classification models with Mass spectrometry (MS) data. Some Machine Learning approaches have been investigated but these ...