AIMC Topic: Neural Networks, Computer

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Network attractors and nonlinear dynamics of neural computation.

Current opinion in neurobiology
The importance of understanding the nonlinear dynamics of neural systems, and the relation to cognitive systems more generally, has been recognised for a long time. Approaches that analyse neural systems in terms of attractors of autonomous networks ...

Deep-Interior: A new pathway to interior tomographic image reconstruction via a weighted backprojection and deep learning.

Medical physics
BACKGROUND: In recent years, deep learning strategies have been combined with either the filtered backprojection or iterative methods or the direct projection-to-image by deep learning only to reconstruct images. Some of these methods can be applied ...

Development and validation of open-source deep neural networks for comprehensive chest x-ray reading: a retrospective, multicentre study.

The Lancet. Digital health
BACKGROUND: Artificial intelligence (AI) systems for automated chest x-ray interpretation hold promise for standardising reporting and reducing delays in health systems with shortages of trained radiologists. Yet, there are few freely accessible AI s...

Diagnosing lagophthalmos using artificial intelligence.

Scientific reports
Lagophthalmos is the incomplete closure of the eyelids posing the risk of corneal ulceration and blindness. Lagophthalmos is a common symptom of various pathologies. We aimed to program a convolutional neural network to automatize lagophthalmos diagn...

Model-based deep learning framework for accelerated optical projection tomography.

Scientific reports
In this work, we propose a model-based deep learning reconstruction algorithm for optical projection tomography (ToMoDL), to greatly reduce acquisition and reconstruction times. The proposed method iterates over a data consistency step and an image d...

SCA-Former: transformer-like network based on stream-cross attention for medical image segmentation.

Physics in medicine and biology
. Deep convolutional neural networks (CNNs) have been widely applied in medical image analysis and achieved satisfactory performances. While most CNN-based methods exhibit strong feature representation capabilities, they face challenges in encoding l...

The automatic diagnosis artificial intelligence system for preoperative magnetic resonance imaging of uterine sarcoma.

Journal of gynecologic oncology
OBJECTIVE: Magnetic resonance imaging (MRI) is efficient for the diagnosis of preoperative uterine sarcoma; however, misdiagnoses may occur. In this study, we developed a new artificial intelligence (AI) system to overcome the limitations of requirin...

Detection of the separated endodontic instrument on periapical radiographs using a deep learning-based convolutional neural network algorithm.

Australian endodontic journal : the journal of the Australian Society of Endodontology Inc
The study evaluated the diagnostic performance of an artificial intelligence system to detect separated endodontic instruments on periapical radiograph radiographs. Three hundred seven periapical radiographs were collected and divided into 222 for tr...

A neural network model for rapid prediction of analyte focusing in isotachophoresis.

Electrophoresis
We present the development and demonstration of a neural network (NN) model for fast and accurate prediction of whether or not a chosen analyte is focused by an isotachophoresis (ITP) buffer system. The NN model is useful in the rapid evaluation of p...

Asynchronous adaptive event-triggered fault detection for delayed Markov jump neural networks: A delay-variation-dependent approach.

Neural networks : the official journal of the International Neural Network Society
This paper presents a delay-variation-dependent approach to fault detection of a discrete-time Markov jump neural network (MJNN) with a time-varying delay and mismatched modes. The goal is to detect the potential fault of delayed MJNNs by constructin...