AIMC Topic: Neural Networks, Computer

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SERR-U-Net: Squeeze-and-Excitation Residual and Recurrent Block-Based U-Net for Automatic Vessel Segmentation in Retinal Image.

Computational and mathematical methods in medicine
METHODS: A new SERR-U-Net framework for retinal vessel segmentation is proposed, which leverages technologies including Squeeze-and-Excitation (SE), residual module, and recurrent block. First, the convolution layers of encoder and decoder are modifi...

Art Image Processing and Color Objective Evaluation Based on Multicolor Space Convolutional Neural Network.

Computational intelligence and neuroscience
A convolutional neural network's weight sharing feature can significantly reduce the cumbersome degree of the network structure and reduce the number of weights that need to be trained. The model can directly input the original image, without the pro...

A note on computing with Kolmogorov Superpositions without iterations.

Neural networks : the official journal of the International Neural Network Society
We extend Kolmogorov's Superpositions to approximating arbitrary continuous functions with a noniterative approach that can be used by any neural network that uses these superpositions. Our approximation algorithm uses a modified dimension reducing f...

Adversarial text-to-image synthesis: A review.

Neural networks : the official journal of the International Neural Network Society
With the advent of generative adversarial networks, synthesizing images from text descriptions has recently become an active research area. It is a flexible and intuitive way for conditional image generation with significant progress in the last year...

Quantum neuron with real weights.

Neural networks : the official journal of the International Neural Network Society
This paper proposes a new model of a real weights quantum neuron exploiting the so-called quantum parallelism which allows for an exponential speedup of computations. The quantum neurons were trained in a classical-quantum approach, considering the d...

Smoothing neural network for L regularized optimization problem with general convex constraints.

Neural networks : the official journal of the International Neural Network Society
In this paper, we propose a neural network modeled by a differential inclusion to solve a class of discontinuous and nonconvex sparse regression problems with general convex constraints, whose objective function is the sum of a convex but not necessa...

Comparison of UV- and Raman-based monitoring of the Protein A load phase and evaluation of data fusion by PLS models and CNNs.

Biotechnology and bioengineering
A promising application of Process Analytical Technology to the downstream process of monoclonal antibodies (mAbs) is the monitoring of the Protein A load phase as its control promises economic benefits. Different spectroscopic techniques have been e...

MSST-RT: Multi-Stream Spatial-Temporal Relative Transformer for Skeleton-Based Action Recognition.

Sensors (Basel, Switzerland)
Skeleton-based human action recognition has made great progress, especially with the development of a graph convolution network (GCN). The most important work is ST-GCN, which automatically learns both spatial and temporal patterns from skeleton sequ...

Estimation of Tool Wear and Surface Roughness Development Using Deep Learning and Sensors Fusion.

Sensors (Basel, Switzerland)
This paper proposes an estimation approach for tool wear and surface roughness using deep learning and sensor fusion. The one-dimensional convolutional neural network (1D-CNN) is utilized as the estimation model with X- and Y-coordinate vibration sig...

Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning.

Tomography (Ann Arbor, Mich.)
We are developing imaging methods for a co-clinical trial investigating synergy between immunotherapy and radiotherapy. We perform longitudinal micro-computed tomography (micro-CT) of mice to detect lung metastasis after treatment. This work explores...