AIMC Topic: Machine Learning

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Transfer learning may explain pigeons' ability to detect cancer in histopathology.

Bioinspiration & biomimetics
Pigeons' unexpected competence in learning to categorize unseen histopathological images has remained an unexplained discovery for almost a decade (Levenson2015e0141357). Could it be that knowledge transferred from their bird's-eye views of the earth...

Multi-transcriptomics analysis of microvascular invasion-related malignant cells and development of a machine learning-based prognostic model in hepatocellular carcinoma.

Frontiers in immunology
BACKGROUND: Microvascular invasion (MVI) stands as a pivotal pathological hallmark of hepatocellular carcinoma (HCC), closely linked to unfavorable prognosis, early recurrence, and metastatic progression. However, the precise mechanistic underpinning...

DFML: Dynamic Federated Meta-Learning for Rare Disease Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Millions of patients suffer from rare diseases around the world. However, the samples of rare diseases are much smaller than those of common diseases. Hospitals are usually reluctant to share patient information for data fusion due to the sensitivity...

CPU-GPU Cooperative QoS Optimization of Personalized Digital Healthcare Using Machine Learning and Swarm Intelligence.

IEEE/ACM transactions on computational biology and bioinformatics
In recent decades, the rapid advances in information technology have promoted a widespread deployment of medical cyber-physical systems (MCPS), especially in the area of digital healthcare. In digital healthcare, medical edge devices empowered by CPU...

A Multi-Classification Accessment Framework for Reproducible Evaluation of Multimodal Learning in Alzheimer's Disease.

IEEE/ACM transactions on computational biology and bioinformatics
Multimodal learning is widely used in automated early diagnosis of Alzheimer's disease. However, the current studies are based on an assumption that different modalities can provide more complementary information to help classify the samples from the...

Stress Classification and Vital Signs Forecasting for IoT-Health Monitoring.

IEEE/ACM transactions on computational biology and bioinformatics
Health monitoring embedded with intelligence is the demand of the day. In this era of a large population with the emergence of a variety of diseases, the demand for healthcare facilities is high. Yet there is scarcity of medical experts, technicians ...

Study of the Immune Infiltration and Sonic Hedgehog Expression Mechanism in Synovial Tissue of Rheumatoid Arthritis-Related Interstitial Lung Disease under Machine Learning CIBERSORT Algorithm.

Molecular biotechnology
Rheumatoid arthritis-related interstitial lung disease (RA-ILD) is one of the common complications in patients with RA, which affects their quality of life. The CIBERSORT algorithm is widely employed to determine the proportion of immune cells (ICs) ...

Automated remote sleep monitoring needs uncertainty quantification.

Journal of sleep research
Wearable electroencephalography devices emerge as a cost-effective and ergonomic alternative to gold-standard polysomnography, paving the way for better health monitoring and sleep disorder screening. Machine learning allows to automate sleep stage c...

Unraveling the physiological and psychosocial signatures of pain by machine learning.

Med (New York, N.Y.)
BACKGROUND: Pain is a complex subjective experience, strongly impacting health and quality of life. Despite many attempts to find effective solutions, present treatments are generic, often unsuccessful, and present significant side effects. Designing...