AIMC Topic: Deep Learning

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Application of one-class classification using deep learning technique improves the classification of subvisible particles.

Journal of pharmaceutical sciences
Capturing subvisible particles using flow imaging microscopy is useful for evaluating protein aggregates that may induce immunogenicity. Automated labeling is desirable to distinguish harmless components such as silicone oil (SO) from subvisible part...

Assessment of clinical feasibility:offline adaptive radiotherapy for lung cancer utilizing kV iCBCT and UNet++ based deep learning model.

Journal of applied clinical medical physics
BACKGROUND: Lung cancer poses a significant global health challenge. Adaptive radiotherapy (ART) addresses uncertainties due to lung tumor dynamics. We aimed to investigate a comprehensively and systematically validated offline ART regimen with high ...

AI-powered techniques in anatomical imaging: Impacts on veterinary diagnostics and surgery.

Annals of anatomy = Anatomischer Anzeiger : official organ of the Anatomische Gesellschaft
BACKGROUND: Artificial intelligence (AI) is rapidly transforming veterinary diagnostic imaging, offering improved accuracy, speed, and efficiency in analyzing complex anatomical structures. AI-powered systems, including deep learning and convolutiona...

Denoising low-field MR images with a deep learning algorithm based on simulated data from easily accessible open-source software.

Journal of magnetic resonance (San Diego, Calif. : 1997)
In this study, we introduce a denoising method aimed at improving the contrast ratio in low-field MRI (LFMRI) using an advanced 3D deep convolutional residual network model. Our approach employs synthetic brain imaging datasets that closely mimic the...

Integrating Interpretability in Machine Learning and Deep Neural Networks: A Novel Approach to Feature Importance and Outlier Detection in COVID-19 Symptomatology and Vaccine Efficacy.

Viruses
In this study, we introduce a novel approach that integrates interpretability techniques from both traditional machine learning (ML) and deep neural networks (DNN) to quantify feature importance using global and local interpretation methods. Our meth...

Automated Food Weight and Content Estimation Using Computer Vision and AI Algorithms.

Sensors (Basel, Switzerland)
The work aims to leverage computer vision and artificial intelligence technologies to quantify key components in food distribution services. Specifically, it focuses on dish counting, content identification, and portion size estimation in a dining ha...

Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning.

Nature communications
Integrating diverse types of biological data is essential for a holistic understanding of cancer biology, yet it remains challenging due to data heterogeneity, complexity, and sparsity. Addressing this, our study introduces an unsupervised deep learn...

Face mask identification with enhanced cuckoo optimization and deep learning-based faster regional neural network.

Scientific reports
A mask identification and social distance monitoring system using Unmanned Aerial Vehicles (UAV) in the outdoors has been proposed for a health establishment. The above approach performed surveillance of the surrounding area using cameras installed i...

Predicting early mortality in hemodialysis patients: a deep learning approach using a nationwide prospective cohort in South Korea.

Scientific reports
Early mortality after hemodialysis (HD) initiation significantly impacts the longevity of HD patients. This study aimed to quantify the effect sizes of risk factors on mortality using various machine learning approaches. A cohort of 3284 HD patients ...