AIMC Topic: Deep Learning

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Rapid identification of horse oil adulteration based on deep learning infrared spectroscopy detection method.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
As a natural oil, horse oil has unique biological activity ingredients and therapeutic characteristics, which has important application value and market potential in healthcare, food, skin care and other fields. However, fraud is rampant in the horse...

Cooperative multi-task learning and interpretable image biomarkers for glioma grading and molecular subtyping.

Medical image analysis
Deep learning methods have been widely used for various glioma predictions. However, they are usually task-specific, segmentation-dependent and lack of interpretable biomarkers. How to accurately predict the glioma histological grade and molecular su...

MPIC: Exploring alternative approach to standard convolution in deep neural networks.

Neural networks : the official journal of the International Neural Network Society
In the rapidly evolving field of deep learning, Convolutional Neural Networks (CNNs) retain their unique strengths and applicability in processing grid-structured data such as images, despite the surge of Transformer architectures. This paper explore...

A novel lightweight deep learning based approaches for the automatic diagnosis of gastrointestinal disease using image processing and knowledge distillation techniques.

Computer methods and programs in biomedicine
BACKGROUND: Gastrointestinal (GI) diseases pose significant challenges for healthcare systems, largely due to the complexities involved in their detection and treatment. Despite the advancements in deep neural networks, their high computational deman...

Towards safe and reliable deep learning for lung nodule malignancy estimation using out-of-distribution detection.

Computers in biology and medicine
Artificial Intelligence (AI) models may fail or suffer from reduced performance when applied to unseen data that differs from the training data distribution, referred to as dataset shift. Automatic detection of out-of-distribution (OOD) data contribu...

Deep learning enabled rapid classification of yeast species in food by imaging of yeast microcolonies.

Food research international (Ottawa, Ont.)
Diverse species of yeasts are commonly associated with food and food production environments. The contamination of food products by spoilage yeasts poses significant challenges, leading to quality degradation and food loss. Similarly, the introductio...

Trans-m5C: A transformer-based model for predicting 5-methylcytosine (m5C) sites.

Methods (San Diego, Calif.)
5-Methylcytosine (m5C) plays a pivotal role in various RNA metabolic processes, including RNA localization, stability, and translation. Current high-throughput sequencing technologies for m5C site identification are resource-intensive in terms of cos...

Interpretable Deep-Learning p Prediction for Small Molecule Drugs via Atomic Sensitivity Analysis.

Journal of chemical information and modeling
Machine learning (ML) models now play a crucial role in predicting properties essential to drug development, such as a drug's logscale acid-dissociation constant (p). Despite recent architectural advances, these models often generalize poorly to nove...

Research on multi-label recognition of tongue features in stroke patients based on deep learning.

Scientific reports
Stroke has become the leading cause of disability in adults worldwide. Early precise rehabilitation intervention is crucial for the recovery of stroke patients, with the key lying in accurately identifying patients' physical characteristics during th...

Dental bur detection system based on asymmetric double convolution and adaptive feature fusion.

Scientific reports
This study aims to improve the detection of dental burs, which are often undetected due to their minuscule size, slender profile, and substantial manufacturing output. The present study introduces You Only Look Once-Dental bur (YOLO-DB), an innovativ...