AIMC Topic: Neural Networks, Computer

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Evolutionary Deep Attention Convolutional Neural Networks for 2D and 3D Medical Image Segmentation.

Journal of digital imaging
Developing a convolutional neural network (CNN) for medical image segmentation is a complex task, especially when dealing with the limited number of available labelled medical images and computational resources. This task can be even more difficult i...

A Sequential Handwriting Recognition Model Based on a Dynamically Configurable CRNN.

Sensors (Basel, Switzerland)
Handwriting recognition refers to recognizing a handwritten input that includes character(s) or digit(s) based on an image. Because most applications of handwriting recognition in real life contain sequential text in various languages, there is a nee...

Combining genetic risk score with artificial neural network to predict the efficacy of folic acid therapy to hyperhomocysteinemia.

Scientific reports
Artificial neural network (ANN) is the main tool to dig data and was inspired by the human brain and nervous system. Several studies clarified its application in medicine. However, none has applied ANN to predict the efficacy of folic acid treatment ...

Correspondence between neuroevolution and gradient descent.

Nature communications
We show analytically that training a neural network by conditioned stochastic mutation or neuroevolution of its weights is equivalent, in the limit of small mutations, to gradient descent on the loss function in the presence of Gaussian white noise. ...

Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images.

Nature communications
Machine-assisted pathological recognition has been focused on supervised learning (SL) that suffers from a significant annotation bottleneck. We propose a semi-supervised learning (SSL) method based on the mean teacher architecture using 13,111 whole...

Interpretable time-aware and co-occurrence-aware network for medical prediction.

BMC medical informatics and decision making
BACKGROUND: Disease prediction based on electronic health records (EHRs) is essential for personalized healthcare. But it's hard due to the special data structure and the interpretability requirement of methods. The structure of EHR is hierarchical: ...

Individual deformability compensation of soft hydraulic actuators through iterative learning-based neural network.

Bioinspiration & biomimetics
Robotic devices with soft actuators have been developed to realize the effective rehabilitation of patients with motor paralysis by enabling soft and safe interaction. However, the control of such robots is challenging, especially owing to the differ...

A GRU-Based Method for Predicting Intention of Aerial Targets.

Computational intelligence and neuroscience
Since a target's operational intention in air combat is realized by a series of tactical maneuvers, its state presents the characteristics of temporal and dynamic changes. Depending only on a single moment to take inference, the traditional combat in...

Decision support or autonomous artificial intelligence? The case of wrong blood in tube errors.

Clinical chemistry and laboratory medicine
OBJECTIVES: Artificial intelligence (AI) models are increasingly being developed for clinical chemistry applications, however, it is not understood whether human interaction with the models, which may occur once they are implemented, improves or wors...

A novel deep neuroevolution-based image classification method to diagnose coronavirus disease (COVID-19).

Computers in biology and medicine
COVID-19 has had a detrimental impact on normal activities, public safety, and the global financial system. To identify the presence of this disease within communities and to commence the management of infected patients early, positive cases should b...