AIMC Topic: Deep Learning
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Impact of deep learning reconstructions on image quality and liver lesion detectability in dual-energy CT: An anthropomorphic phantom study.
Medical physics
Jan 30, 2025
BACKGROUND: Deep learning image reconstruction (DLIR) algorithms allow strong noise reduction while preserving noise texture, which may potentially improve hypervascular focal liver lesions.
Super-resolution deep-learning reconstruction with 1024 matrix improves CT image quality for pancreatic ductal adenocarcinoma assessment.
European journal of radiology
Jan 30, 2025
OBJECTIVES: To evaluate the efficiency of super-resolution deep-learning reconstruction (SR-DLR) optimized for helical body imaging in assessing pancreatic ductal adenocarcinoma (PDAC) using normal-resolution (NR) CT scanner.
UK Biobank MRI data can power the development of generalizable brain clocks: A study of standard ML/DL methodologies and performance analysis on external databases.
NeuroImage
Jan 30, 2025
In this study, we present a comprehensive pipeline to train and compare a broad spectrum of machine learning and deep learning brain clocks, integrating diverse preprocessing strategies and correction terms. Our analysis also includes established met...
The use of artificial intelligence to aid the diagnosis of lung cancer - A retrospective-cohort study.
Radiography (London, England : 1995)
Jan 30, 2025
INTRODUCTION: AI software in the form of deep learning-based automatic detection (DLAD) algorithms for chest X-ray (CXR) interpretation have shown success in early detection of lung cancer (LC), however, there remains uncertainty related to clinical ...
High resolution multi-delay arterial spin labeling with self-supervised deep learning denoising for pediatric choroid plexus perfusion MRI.
NeuroImage
Jan 30, 2025
Choroid plexus (CP) is an important brain structure that produces cerebrospinal fluid (CSF). CP perfusion has been studied using multi-delay arterial spin labeling (MD-ASL) in adults but not in pediatric populations due to the challenge of small CP s...
Can Focusing on One Deep Learning Architecture Improve Fault Diagnosis Performance?
Journal of chemical information and modeling
Jan 30, 2025
Machine learning approaches often involve evaluating a wide range of models due to various available architectures. This standard strategy can lead to a lack of depth in exploring established methods. In this study, we concentrated our efforts on a s...
A robust deep learning model for predicting green tea moisture content during fixation using near-infrared spectroscopy: Integration of multi-scale feature fusion and attention mechanisms.
Food research international (Ottawa, Ont.)
Jan 30, 2025
Fixation is a critical step in green tea processing, and the moisture content of the leaves after fixation is a key indicator of the fixation quality. Near-infrared spectroscopy (NIRS)-based moisture detection technology is often applied in the tea p...
Biomedical named entity recognition using improved green anaconda-assisted Bi-GRU-based hierarchical ResNet model.
BMC bioinformatics
Jan 30, 2025
BACKGROUND: Biomedical text mining is a technique that extracts essential information from scientific articles using named entity recognition (NER). Traditional NER methods rely on dictionaries, rules, or curated corpora, which may not always be acce...
Hierarchical image classification using transfer learning to improve deep learning model performance for amazon parrots.
Scientific reports
Jan 30, 2025
Numerous studies have proven the potential of deep learning models for classifying wildlife. Such models can reduce the workload of experts by automating species classification to monitor wild populations and global trade. Although deep learning mode...
Deep learning-based malaria parasite detection: convolutional neural networks model for accurate species identification of Plasmodium falciparum and Plasmodium vivax.
Scientific reports
Jan 30, 2025
Accurate malaria diagnosis with precise identification of Plasmodium species is crucial for an effective treatment. While microscopy is still the gold standard in malaria diagnosis, it relies heavily on trained personnel. Artificial intelligence (AI)...
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