AIMC Topic: Humans

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Open-set long-tailed recognition via orthogonal prototype learning and false rejection correction.

Neural networks : the official journal of the International Neural Network Society
Learning from data with long-tailed and open-ended distributions is highly challenging. In this work, we propose OLPR, which is a new dual-stream Open-set Long-tailed recognition framework based on orthogonal Prototype learning and false Rejection co...

Graph explicit pooling for graph-level representation learning.

Neural networks : the official journal of the International Neural Network Society
Graph pooling has been increasingly recognized as crucial for Graph Neural Networks (GNNs) to facilitate hierarchical graph representation learning. Existing graph pooling methods commonly consist of two stages: selecting top-ranked nodes and discard...

HAGMN-UQ: Hyper association graph matching network with uncertainty quantification for coronary artery semantic labeling.

Medical image analysis
Coronary artery disease (CAD) is one of the leading causes of death worldwide. Accurate extraction of individual arterial branches from invasive coronary angiograms (ICA) is critical for CAD diagnosis and detection of stenosis. Generating semantic se...

A novel approach to the cause of death identification-multi-strategy integration of multi-organ FTIR spectroscopy information using machine learning.

Talanta
Identifying the cause of death has always been a major focus and challenge in forensic practice and research. Traditional techniques for determining the causes of death are time-consuming, labor-intensive, have high professional barriers, and are vul...

Development and application of explainable artificial intelligence using machine learning classification for long-term facial nerve function after vestibular schwannoma surgery.

Journal of neuro-oncology
PURPOSE: Vestibular schwannomas (VSs) represent the most common cerebellopontine angle tumors, posing a challenge in preserving facial nerve (FN) function during surgery. We employed the Extreme Gradient Boosting machine learning classifier to predic...

Integrative multi-omic and machine learning approach for prognostic stratification and therapeutic targeting in lung squamous cell carcinoma.

BioFactors (Oxford, England)
The proliferation, metastasis, and drug resistance of cancer cells pose significant challenges to the treatment of lung squamous cell carcinoma (LUSC). However, there is a lack of optimal predictive models that can accurately forecast patient prognos...

Sensor technology and machine learning to guide clinical decision making in plastic surgery.

Journal of plastic, reconstructive & aesthetic surgery : JPRAS
Subjective clinical evaluations are deeply rooted in medical practice. Recent advances in sensor technology facilitate the acquisition of extensive amounts of objective physiological data that can serve as a surrogate for subjective assessments. Alon...

Artificial intelligence and informatics in neonatal resuscitation.

Seminars in perinatology
Neonatal intensive care unit resuscitative care continually evolves and increasingly relies on data. Data driven precision resuscitation care can be enabled by leveraging informatics tools and artificial intelligence. Despite technological advancemen...

Early prediction of sudden cardiac death using multimodal fusion of ECG Features extracted from Hilbert-Huang and wavelet transforms with explainable vision transformer and CNN models.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Sudden cardiac death (SCD) is a critical health issue characterized by the sudden failure of heart function, often caused by ventricular fibrillation (VF). Early prediction of SCD is crucial to enable timely interventions. H...

Combining bioinformatics and machine learning to identify diagnostic biomarkers of TB associated with immune cell infiltration.

Tuberculosis (Edinburgh, Scotland)
OBJECTIVE: The asymptomatic nature of tuberculosis (TB) during its latent phase, combined with limitations in current diagnostic methods, makes accurate diagnosis challenging. This study aims to identify TB diagnostic biomarkers by integrating gene e...