AIMC Topic: Deep Learning

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Application and performance of artificial intelligence technology in forensic odontology - A systematic review.

Legal medicine (Tokyo, Japan)
Forensic odontology (FO) mainly deals with the identification of the individual through the remains, which mainly includes teeth and jawbones. Artificial intelligence (AI) technology has proven to be a breakthrough in providing reliable information i...

De-Identification of Facial Features in Magnetic Resonance Images: Software Development Using Deep Learning Technology.

Journal of medical Internet research
BACKGROUND: High-resolution medical images that include facial regions can be used to recognize the subject's face when reconstructing 3-dimensional (3D)-rendered images from 2-dimensional (2D) sequential images, which might constitute a risk of infr...

A novel deep learning conditional generative adversarial network for producing angiography images from retinal fundus photographs.

Scientific reports
Fluorescein angiography (FA) is a procedure used to image the vascular structure of the retina and requires the insertion of an exogenous dye with potential adverse side effects. Currently, there is only one alternative non-invasive system based on O...

RBPsuite: RNA-protein binding sites prediction suite based on deep learning.

BMC genomics
BACKGROUND: RNA-binding proteins (RBPs) play crucial roles in various biological processes. Deep learning-based methods have been demonstrated powerful on predicting RBP sites on RNAs. However, the training of deep learning models is very time-intens...

DeepRetina: Layer Segmentation of Retina in OCT Images Using Deep Learning.

Translational vision science & technology
PURPOSE: To automate the segmentation of retinal layers, we propose DeepRetina, a method based on deep neural networks.

Iterative reconstruction and deep learning algorithms for enabling low-dose computed tomography in midfacial trauma.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVES: The objective of this study was to quantitatively assess the image quality of Advanced Modeled Iterative Reconstruction (ADMIRE) and the PixelShine (PS) deep learning algorithm for the optimization of low-dose computed tomography protocol...

Using Deep Learning Artificial Intelligence Algorithms to Verify N-Nitroso-N-Methylurea and Urethane Positive Control Proliferative Changes in Tg-RasH2 Mouse Carcinogenicity Studies.

Toxicologic pathology
In Tg-rasH2 carcinogenicity mouse models, a positive control group is treated with a carcinogen such as urethane or N-nitroso-N-methylurea to test study validity based on the presence of the expected proliferative lesions in the transgenic mice. We h...

Modular deep reinforcement learning from reward and punishment for robot navigation.

Neural networks : the official journal of the International Neural Network Society
Modular Reinforcement Learning decomposes a monolithic task into several tasks with sub-goals and learns each one in parallel to solve the original problem. Such learning patterns can be traced in the brains of animals. Recent evidence in neuroscienc...

Resilient asynchronous state estimation of Markov switching neural networks: A hierarchical structure approach.

Neural networks : the official journal of the International Neural Network Society
This paper deals with the issue of resilient asynchronous state estimation of discrete-time Markov switching neural networks. Randomly occurring signal quantization and packet dropout are involved in the imperfect measured output. The asynchronous sw...