AIMC Topic: Deep Learning

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GARNN: An interpretable graph attentive recurrent neural network for predicting blood glucose levels via multivariate time series.

Neural networks : the official journal of the International Neural Network Society Feb 5, 2025
Accurate prediction of future blood glucose (BG) levels can effectively improve BG management for people living with type 1 or 2 diabetes, thereby reducing complications and improving quality of life. The state of the art of BG prediction has been ac...
Recurrent Neural Networks Deep Learning Time Factors Blood Glucose Humans Neural Networks, Computer Diabetes Mellitus, Type 2 Diabetes Mellitus, Type 1
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CGNet: Few-shot learning for Intracranial Hemorrhage Segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Feb 5, 2025
In recent years, with the increasing attention from researchers towards medical imaging, deep learning-based image segmentation techniques have become mainstream in the field, requiring large amounts of manually annotated data. Annotating datasets fo...
Intracranial Hemorrhages Algorithms Deep Learning Image Processing, Computer-Assisted Humans
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Protein ligand structure prediction: From empirical to deep learning approaches.

Current opinion in structural biology Feb 5, 2025
Protein-ligand structure prediction methods, aiming to predict the three-dimensional complex structure and binding energy of a compound and target protein, are essential in many structure-based drug discovery pipelines, including virtual screening an...
Drug Discovery Computational Biology Proteins Models, Molecular Deep Learning Protein Binding Ligands Protein Conformation
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Fast In Vivo Two-Photon Fluorescence Imaging via Lateral and Axial Resolution Restoration With Self-Supervised Learning.

Journal of biophotonics Feb 5, 2025
Two-photon fluorescence (TPF) imaging opens a new avenue to achieve high resolution at extended penetration depths. However, it is difficult for conventional TPF imaging systems to simultaneously achieve high resolution and speed. In this work, we de...
Time Factors Image Processing, Computer-Assisted Optical Imaging Animals Supervised Machine Learning Mice Microscopy, Fluorescence, Multiphoton Deep Learning Signal-To-Noise Ratio
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Association between deep learning radiomics based on placental MRI and preeclampsia with fetal growth restriction: A multicenter study.

European journal of radiology Feb 5, 2025
PURPOSE: Preeclampsia (PE) is associated with placental insufficiency and could lead to adverse pregnancy outcomes. The study aimed to develop a placental T2-weighted image-based automatic quantitative model for the identification of PE pregnancies a...
Fetal Growth Retardation Adult Pre-Eclampsia Pregnancy Humans Radiomics Image Interpretation, Computer-Assisted Magnetic Resonance Imaging Female Retrospective Studies Deep Learning Placenta
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Deep Learning Enhances Precision of Citrullination Identification in Human and Plant Tissue Proteomes.

Molecular & cellular proteomics : MCP Feb 5, 2025
Citrullination is a critical yet understudied post-translational modification (PTM) implicated in various biological processes. Exploring its role in health and disease requires a comprehensive understanding of the prevalence of this PTM at a proteom...
Humans Proteomics Proteome Deep Learning Protein Processing, Post-Translational Arabidopsis Citrullination Plant Proteins
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Combining deep learning and machine learning techniques to track air pollution in relation to vegetation cover utilizing remotely sensed data.

Journal of environmental management Feb 5, 2025
The rapid urban expansion in Dhaka, the capital of Bangladesh, has escalated air pollution levels and led to a significant decrease in green spaces. This study employed machine learning (ML) and deep learning (DL) techniques to examine the relationsh...
Particulate Matter Bangladesh Deep Learning Environmental Monitoring Remote Sensing Technology Air Pollution Machine Learning Air Pollutants
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You get the best of both worlds? Integrating deep learning and traditional machine learning for breast cancer risk prediction.

Computers in biology and medicine Feb 5, 2025
Breast Cancer is the most commonly diagnosed cancer worldwide. While screening mammography diminishes the burden of this disease, it has some flaws related to the presence of false negatives. Adapting screening to each woman's needs could help overco...
Early Detection of Cancer Mammography Case-Control Studies Deep Learning Aged Adult Middle Aged Risk Assessment Breast Neoplasms Humans Machine Learning Female
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Deep Learning-Enhanced Chemiluminescence Vertical Flow Assay for High-Sensitivity Cardiac Troponin I Testing.

Small (Weinheim an der Bergstrasse, Germany) Feb 5, 2025
Democratizing biomarker testing at the point-of-care requires innovations that match laboratory-grade sensitivity and precision in an accessible format. Here, high-sensitivity detection of cardiac troponin I (cTnI) is demonstrated through innovations...
Limit of Detection Myocardium Myocardial Infarction Troponin I Luminescent Measurements Humans Biomarkers Deep Learning
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Fuzz Testing Molecular Representation Using Deep Variational Anomaly Generation.

Journal of chemical information and modeling Feb 5, 2025
Researchers are developing increasingly robust molecular representations, motivating the need for thorough methods to stress-test and validate them. Here, we use a variational auto-encoder (VAE), an unsupervised deep learning model, to generate anoma...
Deep Learning
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