AIMC Topic: Deep Learning

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Multimodal deep learning: tumor and visceral fat impact on colorectal cancer occult peritoneal metastasis.

European radiology
OBJECTIVES: This study proposes a multimodal deep learning (DL) approach to investigate the impact of tumors and visceral fat on occult peritoneal metastasis in colorectal cancer (CRC) patients.

Rapid wall shear stress prediction for aortic aneurysms using deep learning: a fast alternative to CFD.

Medical & biological engineering & computing
Aortic aneurysms pose a significant risk of rupture. Previous research has shown that areas exposed to low wall shear stress (WSS) are more prone to rupture. Therefore, precise WSS determination on the aneurysm is crucial for rupture risk assessment....

Non-destructive origin and ginsenoside analysis of American ginseng via NIR and deep learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
American ginseng is widely in demand as a famous medicinal herb, but the production conditions affect the content of ginsenosides in American ginseng, which in turn affects its medicinal value. Currently, it remains a challenge to simultaneously iden...

A deep transfer learning approach for Real-Time traffic conflict prediction with trajectory data.

Accident; analysis and prevention
Recently, real-time traffic conflict prediction has drawn increasing attention due to its significant potential in proactive traffic safety systems. While various statistical and machine learning models have been developed for conflict prediction, tr...

Application of deep learning for evaluation of the growth rate of Daphnia magna.

Journal of bioscience and bioengineering
For the safe use of chemicals widely used in human activities, it is crucial to assess their ecological impacts when released into the environment. Daphnia, a well-established environmental indicator species, is commonly used to evaluate the biologic...

FusionNet: Dual input feature fusion network with ensemble based filter feature selection for enhanced brain tumor classification.

Brain research
Brain tumors pose a significant threat to human health, require a precise and quick diagnosis for effective treatment. However, achieving high diagnostic accuracy with traditional methods remains challenging due to the complex nature of brain tumors....

TimePAD─Unveiling Temporal Sequence ELISA Signal by Deep Learning for Rapid Readout and Improved Accuracy in a Microfluidic Paper-Based Analytical Platform.

Analytical chemistry
The integration of paper-based microfluidics with deep learning represents a pivotal trend in enhancing diagnostic capabilities. This paper introduces a new approach to improve the performance of a paper-based microfluidic enzyme-linked immunosorbent...

MARBLE: interpretable representations of neural population dynamics using geometric deep learning.

Nature methods
The dynamics of neuron populations commonly evolve on low-dimensional manifolds. Thus, we need methods that learn the dynamical processes over neural manifolds to infer interpretable and consistent latent representations. We introduce a representatio...

A Multimodal Pain Sentiment Analysis System Using Ensembled Deep Learning Approaches for IoT-Enabled Healthcare Framework.

Sensors (Basel, Switzerland)
This study introduces a multimodal sentiment analysis system to assess and recognize human pain sentiments within an Internet of Things (IoT)-enabled healthcare framework. This system integrates facial expressions and speech-audio recordings to evalu...