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Deep Learning - AI Medical Compendium

AIMC Topic: Deep Learning

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Synthesized colonoscopy dataset from high-fidelity virtual colon with abnormal simulation.

Computers in biology and medicine Jan 17, 2025
With the advent of the deep learning-based colonoscopy system, the need for a vast amount of high-quality colonoscopy image datasets for training is crucial. However, the generalization ability of deep learning models is challenged by the limited ava...
Humans Databases, Factual Colon Imaging, Three-Dimensional Colonoscopy Colonic Polyps Deep Learning Colonography, Computed Tomographic
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An intelligent fruit freshness monitoring system using hydrophobic indicator labels based on methylcellulose, k-carrageenan, and sodium tripolyphosphate, combined with deep learning.

International journal of biological macromolecules Jan 17, 2025
As the demand for food quality and safety continues to rise, pH-responsive intelligent packaging technologies have found widespread application in the monitoring of food freshness. This study introduces a methylcellulose (MC)-based indicator label de...
Hydrophobic and Hydrophilic Interactions Carrageenan Food Quality Polyphosphates Food Packaging Deep Learning Methylcellulose Fruit
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Explainable deep learning and virtual evolution identifies antimicrobial peptides with activity against multidrug-resistant human pathogens.

Nature microbiology Jan 17, 2025
Artificial intelligence (AI) is a promising approach to identify new antimicrobial compounds in diverse microbial species. Here we developed an AI-based, explainable deep learning model, EvoGradient, that predicts the potency of antimicrobial peptide...
Enterococcus faecium Antimicrobial Peptides Bacteria Escherichia coli Acinetobacter baumannii Mice Klebsiella pneumoniae Humans Deep Learning Anti-Bacterial Agents Microbial Sensitivity Tests Animals Drug Resistance, Multiple, Bacterial
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The Application of an Intelligent -Harvesting Device Based on FES-YOLOv5s.

Sensors (Basel, Switzerland) Jan 17, 2025
To address several challenges, including low efficiency, significant damage, and high costs, associated with the manual harvesting of , in this study, a machine vision-based intelligent harvesting device was designed according to its agronomic charac...
Algorithms Robotics Agaricus Deep Learning
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Structure-Based Approaches for Protein-Protein Interaction Prediction Using Machine Learning and Deep Learning.

Biomolecules Jan 17, 2025
Protein-Protein Interaction (PPI) prediction plays a pivotal role in understanding cellular processes and uncovering molecular mechanisms underlying health and disease. Structure-based PPI prediction has emerged as a robust alternative to sequence-ba...
Machine Learning Protein Interaction Mapping Proteomics Deep Learning Humans Proteins Computational Biology Protein Conformation
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Automatic segmentation of MRI images for brain radiotherapy planning using deep ensemble learning.

Biomedical physics & engineering express Jan 17, 2025
This study aimed to develop and evaluate an efficient method to automatically segment T1- and T2-weighted brain magnetic resonance imaging (MRI) images. We specifically compared the segmentation performance of individual convolutional neural network ...
Ensemble Learning Algorithms Brain Brain Neoplasms Humans Neural Networks, Computer Deep Learning Image Processing, Computer-Assisted Organs at Risk Magnetic Resonance Imaging Radiotherapy Planning, Computer-Assisted Radiotherapy, Image-Guided
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Clinical feasibility of deep learning-driven magnetic resonance angiography collateral map in acute anterior circulation ischemic stroke.

Scientific reports Jan 17, 2025
To validate the clinical feasibility of deep learning-driven magnetic resonance angiography (DL-driven MRA) collateral map in acute ischemic stroke. We employed a 3D multitask regression and ordinal regression deep neural network, called as 3D-MROD-N...
Ischemic Stroke Feasibility Studies Magnetic Resonance Angiography Male Middle Aged Deep Learning Cerebrovascular Circulation Collateral Circulation Humans Female Aged Aged, 80 and over
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Interpretable and integrative deep learning for discovering brain-behaviour associations.

Scientific reports Jan 17, 2025
Recent advances highlight the limitations of classification strategies in machine learning that rely on a single data source for understanding, diagnosing and predicting psychiatric syndromes. Moreover, approaches based solely on clinician labels oft...
Mental Disorders Brain Female Humans Deep Learning Magnetic Resonance Imaging Adult Male
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Exploring the anticancer activities of Sulfur and magnesium oxide through integration of deep learning and fuzzy rough set analyses based on the features of Vidarabine alkaloid.

Scientific reports Jan 17, 2025
Drug discovery and development is a challenging and time-consuming process. Laboratory experiments conducted on Vidarabine showed IC 6.97 µg∕mL, 25.78 µg∕mL, and ˃ 100 µg∕mL against non-small Lung cancer (A-549), Human Melanoma (A-375), and Human epi...
Antineoplastic Agents Humans Deep Learning Vidarabine Cell Line, Tumor Fuzzy Logic Magnesium Oxide Sulfur
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Biologically relevant integration of transcriptomics profiles from cancer cell lines, patient-derived xenografts, and clinical tumors using deep learning.

Science advances Jan 17, 2025
Cell lines and patient-derived xenografts are essential to cancer research; however, the results derived from such models often lack clinical translatability, as they do not fully recapitulate the complex cancer biology. Identifying preclinical model...
Neoplasms Gene Expression Profiling Transcriptome Humans Animals Gene Expression Regulation, Neoplastic Xenograft Model Antitumor Assays Heterografts Deep Learning Cell Line, Tumor Mice
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