AIMC Topic: Deep Learning

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CDSNet: An automated method for assessing growth stages from various anatomical regions in lateral cephalograms based on deep learning.

Journal of the World federation of orthodontists
BACKGROUND: The assessment of growth stages, typically determined by Cervical Vertebrae Maturation (CVM), plays a crucial role in orthodontics. However, there is a potential deviation from actual growth stages when using CVM. This study aimed to intr...

Accelerated Spine MRI with Deep Learning Based Image Reconstruction: A Prospective Comparison with Standard MRI.

Academic radiology
RATIONALE AND OBJECTIVES: To evaluate the performance of deep learning (DL) reconstructed MRI in terms of image acquisition time, overall image quality and diagnostic interchangeability compared to standard-of-care (SOC) MRI.

Deep graph clustering via aligning representation learning.

Neural networks : the official journal of the International Neural Network Society
Deep graph clustering is a fundamental yet challenging task for graph data analysis. Recent efforts have witnessed significant success in combining autoencoder and graph convolutional network to explore graph-structured data. However, we observe that...

Antigen-independent single-cell circulating tumor cell detection using deep-learning-assisted biolasers.

Biosensors & bioelectronics
Circulating tumor cells (CTCs) in the bloodstream are important biomarkers for clinical prognosis of cancers. Current CTC identification methods are based on immuno-labeling, which depends on the differential expression of specific antigens between t...

EfficientNet-resDDSC: A Hybrid Deep Learning Model Integrating Residual Blocks and Dilated Convolutions for Inferring Gene Causality in Single-Cell Data.

Interdisciplinary sciences, computational life sciences
Gene Regulatory Networks (GRNs) reveal complex interactions between genes in organisms, crucial for understanding the life system's operation. The rapid development of biotechnology, especially single-cell RNA sequencing (scRNA-seq), has generated a ...

Multi-task magnetic resonance imaging reconstruction using meta-learning.

Magnetic resonance imaging
Using single-task deep learning methods to reconstruct Magnetic Resonance Imaging (MRI) data acquired with different imaging sequences is inherently challenging. The trained deep learning model typically lacks generalizability, and the dissimilarity ...

A deep neural network improves endoscopic detection of laterally spreading tumors.

Surgical endoscopy
BACKGROUND: Colorectal cancer (CRC) is the malignant tumor of the digestive system with the highest incidence and mortality rate worldwide. Laterally spreading tumors (LSTs) of the large intestine have unique morphological characteristics, special gr...

Implementing deep learning on edge devices for snoring detection and reduction.

Computers in biology and medicine
This study introduces MinSnore, a novel deep learning model tailored for real-time snoring detection and reduction, specifically designed for deployment on low-configuration edge devices. By integrating MobileViTV3 blocks into the Dynamic MobileNetV3...

Multi-scale multimodal deep learning framework for Alzheimer's disease diagnosis.

Computers in biology and medicine
Multimodal neuroimaging data, including magnetic resonance imaging (MRI) and positron emission tomography (PET), provides complementary information about the brain that can aid in Alzheimer's disease (AD) diagnosis. However, most existing deep learni...

Deep learning-based denoising for unbiased analysis of morphology and stiffness in amyloid fibrils.

Computers in biology and medicine
Understanding the morphology of amyloid fibrils is crucial for comprehending the aggregation and degradation mechanisms of abnormal proteins implicated in various diseases, such as Alzheimer's disease, Parkinson's disease, type II diabetes, and vario...