AIMC Topic: Deep Learning

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3D Hyperspectral Data Analysis with Spatially Aware Deep Learning for Diagnostic Applications.

Analytical chemistry
Nowadays, with the rise of artificial intelligence (AI), deep learning algorithms play an increasingly important role in various traditional fields of research. Recently, these algorithms have already spread into data analysis for Raman spectroscopy....

Application of Deep Learning to Predict the Persistence, Bioaccumulation, and Toxicity of Pharmaceuticals.

Journal of chemical information and modeling
This study investigates the application of a deep learning (DL) model, specifically a message-passing neural network (MPNN) implemented through Chemprop, to predict the persistence, bioaccumulation, and toxicity (PBT) characteristics of compounds, wi...

ErgoReport: A Holistic Posture Assessment Framework Based on Inertial Data and Deep Learning.

Sensors (Basel, Switzerland)
Awkward postures are a significant contributor to work-related musculoskeletal disorders (WRMSDs), which represent great social and economic burdens. Various posture assessment tools assess WRMSD risk but fall short in providing an elucidating risk b...

Finger Vein Recognition Based on Unsupervised Spiking Convolutional Neural Network with Adaptive Firing Threshold.

Sensors (Basel, Switzerland)
Currently, finger vein recognition (FVR) stands as a pioneering biometric technology, with convolutional neural networks (CNNs) and Transformers, among other advanced deep neural networks (DNNs), consistently pushing the boundaries of recognition acc...

Deep learning assisted retinal microvasculature assessment and cerebral small vessel disease in Fabry disease.

Orphanet journal of rare diseases
PURPOSE: The aim of this study was to assess retinal microvascular parameters (RMPs) in Fabry disease (FD) using deep learning, and analyze the correlation with brain lesions related to cerebral small vessel disease (CSVD).

CausalCervixNet: convolutional neural networks with causal insight (CICNN) in cervical cancer cell classification-leveraging deep learning models for enhanced diagnostic accuracy.

BMC cancer
Cervical cancer is a significant global health issue affecting women worldwide, necessitating prompt detection and effective management. According to the World Health Organization (WHO), approximately 660,000 new cases of cervical cancer and 350,000 ...

An enhanced CNN-Bi-transformer based framework for detection of neurological illnesses through neurocardiac data fusion.

Scientific reports
Classical approaches to diagnosis frequently rely on self-reported symptoms or clinician observations, which can make it difficult to examine mental health illnesses due to their subjective and complicated nature. In this work, we offer an innovative...

CodonTransformer: a multispecies codon optimizer using context-aware neural networks.

Nature communications
Degeneracy in the genetic code allows many possible DNA sequences to encode the same protein. Optimizing codon usage within a sequence to meet organism-specific preferences faces combinatorial explosion. Nevertheless, natural sequences optimized thro...

GCN-BBB: Deep Learning Blood-Brain Barrier (BBB) Permeability PharmacoAnalytics with Graph Convolutional Neural (GCN) Network.

The AAPS journal
The Blood-Brain Barrier (BBB) is a selective barrier between the Central Nervous System (CNS) and the peripheral system, regulating the distribution of molecules. BBB permeability has been crucial in CNS-targeting drug development, such as glioblasto...

CTUSurv: A Cell-Aware Transformer-Based Network With Uncertainty for Survival Prediction Using Whole Slide Images.

IEEE transactions on medical imaging
Image-based survival prediction through deep learning techniques represents a burgeoning frontier aimed at augmenting the diagnostic capabilities of pathologists. However, directly applying existing deep learning models to survival prediction may not...