AIMC Topic: Deep Learning

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Comparative Analysis of the Diagnostic Value of S-Detect Technology in Different Planes Versus the BI-RADS Classification for Breast Lesions.

Academic radiology
RATIONALE AND OBJECTIVES: S-Detect, a deep learning-based Computer-Aided Detection system, is recognized as an important tool for diagnosing breast lesions using ultrasound imaging. However, it may exhibit inconsistent findings across multiple imagin...

Foundation models in gastrointestinal endoscopic AI: Impact of architecture, pre-training approach and data efficiency.

Medical image analysis
Pre-training deep learning models with large data sets of natural images, such as ImageNet, has become the standard for endoscopic image analysis. This approach is generally superior to training from scratch, due to the scarcity of high-quality medic...

Deformation depth decoupling network for point cloud domain adaptation.

Neural networks : the official journal of the International Neural Network Society
Recently, point cloud domain adaptation (DA) practices have been implemented to improve the generalization ability of deep learning models on point cloud data. However, variations across domains often result in decreased performance of models trained...

Pupillometry as a biomarker of postural control: Deep-learning models reveal side-specific pupillary responses to increased intensity of balance tasks.

Psychophysiology
Pupillometry has been used in the studies of postural control to assess cognitive load during dual tasks, but its response to increased balance task intensity has not been investigated. Furthermore, it is unknown whether side-specific changes in pupi...

Wavelet-based selection-and-recalibration network for Parkinson's disease screening in OCT images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Parkinson's disease (PD) is one of the most prevalent neurodegenerative brain diseases worldwide. Therefore, accurate PD screening is crucial for early clinical intervention and treatment. Recent clinical research indicates ...

Deep learning radiomics based on ultrasound images for the assisted diagnosis of chronic kidney disease.

Nephrology (Carlton, Vic.)
AIM: This study aimed to explore the value of ultrasound (US) images in chronic kidney disease (CKD) screening by constructing a CKD screening model based on grey-scale US images.

Deep learning models for separate segmentations of intracerebral and intraventricular hemorrhage on head CT and segmentation quality assessment.

Medical physics
BACKGROUND: The volume measurement of intracerebral hemorrhage (ICH) and intraventricular hemorrhage (IVH) provides critical information for precise treatment of patients with spontaneous ICH but remains a big challenge, especially for IVH segmentati...

Accurate Identification of Cancer Cells in Complex Pre-Clinical Models Using a Deep-Learning Neural Network: A Transfection-Free Approach.

Advanced biology
3D co-cultures are key tools for in vitro biomedical research as they recapitulate more closely the in vivo environment while allowing a tighter control on the culture's composition and experimental conditions. The limited technologies available for ...

Dual-branch Transformer for semi-supervised medical image segmentation.

Journal of applied clinical medical physics
PURPOSE: In recent years, the use of deep learning for medical image segmentation has become a popular trend, but its development also faces some challenges. Firstly, due to the specialized nature of medical data, precise annotation is time-consuming...

Deep learning can predict subgenome dominance in ancient but not in neo/synthetic polyploidized genomes.

The Plant journal : for cell and molecular biology
Deep learning offers new approaches to investigate the mechanisms underlying complex biological phenomena, such as subgenome dominance. Subgenome dominance refers to the dominant expression and/or biased fractionation of genes in one subgenome of all...