AIMC Topic: Deep Learning

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Deep learning for classification of pediatric chest radiographs by WHO's standardized methodology.

PloS one
BACKGROUND: The World Health Organization (WHO)-defined radiological pneumonia is a preferred endpoint in pneumococcal vaccine efficacy and effectiveness studies in children. Automating the WHO methodology may support more widespread application of t...

Feasibility of a deep learning-based algorithm for automated detection and classification of nasal polyps and inverted papillomas on nasal endoscopic images.

International forum of allergy & rhinology
BACKGROUND: Discrimination of nasal cavity mass lesions is a challenging work requiring extensive experience. A deep learning-based automated diagnostic system may help clinicians to classify nasal cavity mass lesions. We demonstrated the feasibility...

Monitoring the vertical distribution of HABs using hyperspectral imagery and deep learning models.

The Science of the total environment
Remote sensing techniques have been applied to monitor the spatiotemporal variation of harmful algal blooms (HABs) in many inland waters. However, these studies have been limited to monitor the vertical distribution of HABs due to the optical complex...

Label-free classification of dead and live colonic adenocarcinoma cells based on 2D light scattering and deep learning analysis.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
The measurement of cell viability plays an essential role in the area of cell biology. At present, the common methods for cell viability assay mainly on the responses of cells to different dyes. However, the additional steps of cell staining will con...

QSAR modeling without descriptors using graph convolutional neural networks: the case of mutagenicity prediction.

Molecular diversity
Deep neural networks are effective in learning directly from low-level encoded data without the need of feature extraction. This paper shows how QSAR models can be constructed from 2D molecular graphs without computing chemical descriptors. Two graph...

A review of medical image data augmentation techniques for deep learning applications.

Journal of medical imaging and radiation oncology
Research in artificial intelligence for radiology and radiotherapy has recently become increasingly reliant on the use of deep learning-based algorithms. While the performance of the models which these algorithms produce can significantly outperform ...

An Autoencoder-Based Deep Learning Approach for Load Identification in Structural Dynamics.

Sensors (Basel, Switzerland)
In civil engineering, different machine learning algorithms have been adopted to process the huge amount of data continuously acquired through sensor networks and solve inverse problems. Challenging issues linked to structural health monitoring or lo...

Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery.

BMC nephrology
BACKGROUND: Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible ...

Advanced deep learning methodology for accurate, real-time segmentation of high-resolution intravascular ultrasound images.

International journal of cardiology
AIMS: The aim of this study is to develop and validate a deep learning (DL) methodology capable of automated and accurate segmentation of intravascular ultrasound (IVUS) image sequences in real-time.