AIMC Topic: Deep Learning

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A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies.

Chemical research in toxicology
Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches hav...

Deep Learning for Counting People from UWB Channel Impulse Response Signals.

Sensors (Basel, Switzerland)
The use of higher frequency bands compared to other wireless communication protocols enhances the capability of accurately determining locations from ultra-wideband (UWB) signals. It can also be used to estimate the number of people in a room based o...

Efficient Photoacoustic Image Synthesis with Deep Learning.

Sensors (Basel, Switzerland)
Photoacoustic imaging potentially allows for the real-time visualization of functional human tissue parameters such as oxygenation but is subject to a challenging underlying quantification problem. While in silico studies have revealed the great pote...

Preoperative Discrimination of CDKN2A/B Homozygous Deletion Status in Isocitrate Dehydrogenase-Mutant Astrocytoma: A Deep Learning-Based Radiomics Model Using MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) homozygous deletion has been verified as an independent and critical biomarker of negative prognosis and short survival in isocitrate dehydrogenase (IDH)-mutant astrocytoma. Therefore, non...

CaMeL-Net: Centroid-aware metric learning for efficient multi-class cancer classification in pathology images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Cancer grading in pathology image analysis is a major task due to its importance in patient care, treatment, and management. The recent developments in artificial neural networks for computational pathology have demonstrated...

Deep learning-based method for predicting and classifying the binding affinity of protein-protein complexes.

Biochimica et biophysica acta. Proteins and proteomics
Protein-protein interactions (PPIs) play a critical role in various biological processes. Accurately estimating the binding affinity of PPIs is essential for understanding the underlying molecular recognition mechanisms. In this study, we employed a ...

Unlocking the Potential of Zebrafish Research with Artificial Intelligence: Advancements in Tracking, Processing, and Visualization.

Medical & biological engineering & computing
Zebrafish have become a widely accepted model organism for biomedical research due to their strong cortisol stress response, behavioral strain differences, and sensitivity to both drug treatments and predators. However, experimental zebrafish studies...

Advances and applications of machine learning and deep learning in environmental ecology and health.

Environmental pollution (Barking, Essex : 1987)
Machine learning (ML) and deep learning (DL) possess excellent advantages in data analysis (e.g., feature extraction, clustering, classification, regression, image recognition and prediction) and risk assessment and management in environmental ecolog...

Using deep learning for an automatic detection and classification of the vascular bifurcations along the Circle of Willis.

Medical image analysis
Most of the intracranial aneurysms (ICA) occur on a specific portion of the cerebral vascular tree named the Circle of Willis (CoW). More particularly, they mainly arise onto fifteen of the major arterial bifurcations constituting this circular struc...

Optimizing detection and deep learning-based classification of pathological high-frequency oscillations in epilepsy.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: This study aimed to explore sensitive detection methods for pathological high-frequency oscillations (HFOs) to improve seizure outcomes in epilepsy surgery.