AI Medical Compendium Topic

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Self-Supervised Learning for Feature Extraction from Glomerular Images and Disease Classification with Minimal Annotations.

Journal of the American Society of Nephrology : JASN
BACKGROUND: Deep learning has great potential in digital kidney pathology. However, its effectiveness depends heavily on the availability of extensively labeled datasets, which are often limited because of the specialized knowledge and time required ...

Determination of quality differences and origin tracing of green tea from different latitudes based on TG-FTIR and machine learning.

Food research international (Ottawa, Ont.)
Latitude differences can significantly affect the quality of tea, while in-depth research in this field is lacking. This study investigates green teas from different latitudes in China using thermogravimetric analysis coupled with infrared spectrosco...

High-throughput, rapid, and non-destructive detection of common foodborne pathogens via hyperspectral imaging coupled with deep neural networks and support vector machines.

Food research international (Ottawa, Ont.)
Foodborne pathogens such as Bacillus cereus, Staphylococcus aureus, and Escherichia coli are major causes of gastrointestinal diseases worldwide and frequently contaminate dairy products. Compared to nucleic acid detection and MALDI-TOF MS, hyperspec...

Exploring the impact of lenticels on the detection of soluble solids content in apples and pears using hyperspectral imaging and one-dimensional convolutional neural networks.

Food research international (Ottawa, Ont.)
In this work, the effect of lenticels on the predictive performance of apple and pear soluble solids content (SSC) models developed based on hyperspectral imaging (HSI) at 380-1010 nm was investigated for the first time. Variations in the spectral pr...

A Novel Improvement of Feature Selection for Dynamic Hand Gesture Identification Based on Double Machine Learning.

Sensors (Basel, Switzerland)
Causal machine learning is an approach that combines causal inference and machine learning to understand and utilize causal relationships in data. In current research and applications, traditional machine learning and deep learning models always focu...

An extensive experimental analysis for heart disease prediction using artificial intelligence techniques.

Scientific reports
The heart is an important organ that plays a crucial role in maintaining life. Unfortunately, heart disease is one of the major causes of mortality globally. Early and accurate detection can significantly improve the situation by enabling preventive ...

Compression-enabled interpretability of voxelwise encoding models.

PLoS computational biology
Voxelwise encoding models based on convolutional neural networks (CNNs) are widely used as predictive models of brain activity evoked by natural movies. Despite their superior predictive performance, the huge number of parameters in CNN-based models ...

An efficient approach on risk factor prediction related to cardiovascular disease around Kumbakonam, Tamil Nadu, India, using unsupervised machine learning techniques.

Scientific reports
Nowadays, human beings suffer from varieties of diseases due to the environmental circumstances and their residing habits. Cardiovascular diseases (CVD) are the leading cause of mortality among all diseases. CVDs are heart-related diseases. In early ...

Contaminant detection in flexible polypropylene packaging waste using hyperspectral imaging and machine learning.

Waste management (New York, N.Y.)
Flexible plastic packaging (FPP) constitutes one of the largest post-consumer plastic streams processed in recycling facilities. To address the key challenges of its sorting and quality control, this study developed and tested a classification proced...

Tracking the spatiotemporal evolution of groundwater chemistry in the Quaternary aquifer system of Debrecen area, Hungary: integration of classical and unsupervised learning methods.

Environmental science and pollution research international
Monitoring changes in groundwater quality over time helps identify time-dependent factors influencing water safety and supports the development of effective management strategies. This study investigates the spatiotemporal evolution of groundwater ch...