AIMC Topic: Algorithms

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Validating deep learning inference during chest X-ray classification for COVID-19 screening.

Scientific reports
The new coronavirus unleashed a worldwide pandemic in early 2020, and a fatality rate several times that of the flu. As the number of infections soared, and capabilities for testing lagged behind, chest X-ray (CXR) imaging became more relevant in the...

Chronic kidney disease diagnosis using decision tree algorithms.

BMC nephrology
BACKGROUND: Chronic Kidney Disease (CKD), i.e., gradual decrease in the renal function spanning over a duration of several months to years without any major symptoms, is a life-threatening disease. It progresses in six stages according to the severit...

Using a machine learning approach to identify key prognostic molecules for esophageal squamous cell carcinoma.

BMC cancer
BACKGROUND: A plethora of prognostic biomarkers for esophageal squamous cell carcinoma (ESCC) that have hitherto been reported are challenged with low reproducibility due to high molecular heterogeneity of ESCC. The purpose of this study was to ident...

Deep learning-based image reconstruction for TOF PET with DIRECT data partitioning format.

Physics in medicine and biology
Conventional positron emission tomography (PET) image reconstruction is achieved by the statistical iterative method. Deep learning provides another opportunity for speeding up the image reconstruction process. However, conventional deep learning-bas...

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...

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...

The effect of principal component analysis in the diagnosis of congestive heart failure via heart rate variability analysis.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
In this study, we investigated the effect of principal component analysis (PCA) in congestive heart failure (CHF) diagnosis using various machine learning algorithms from 5-min HRV data. The extracted 59 heart rate variability (HRV) features consist ...