AIMC Topic: Image Processing, Computer-Assisted

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Scanner model classification with characteristic brightness variations.

Journal of forensic sciences
Analog documents and scanned digitized files are now considered equivalent in legal contexts, and the widespread supply of multi-functional printers has led to a surge in the use of scanned documents. With image editing tools, there has been more cas...

An image classification deep-learning algorithm for shrapnel detection from ultrasound images.

Scientific reports
Ultrasound imaging is essential for non-invasively diagnosing injuries where advanced diagnostics may not be possible. However, image interpretation remains a challenge as proper expertise may not be available. In response, artificial intelligence al...

Symmetric All Convolutional Neural-Network-Based Unsupervised Feature Extraction for Hyperspectral Images Classification.

IEEE transactions on cybernetics
Recently, deep-learning-based feature extraction (FE) methods have shown great potential in hyperspectral image (HSI) processing. Unfortunately, it also brings a challenge that the training of the deep learning networks always requires large amounts ...

Predicting Network Controllability Robustness: A Convolutional Neural Network Approach.

IEEE transactions on cybernetics
Network controllability measures how well a networked system can be controlled to a target state, and its robustness reflects how well the system can maintain the controllability against malicious attacks by means of node removals or edge removals. T...

High-throughput widefield fluorescence imaging of 3D samples using deep learning for 2D projection image restoration.

PloS one
Fluorescence microscopy is a core method for visualizing and quantifying the spatial and temporal dynamics of complex biological processes. While many fluorescent microscopy techniques exist, due to its cost-effectiveness and accessibility, widefield...

Right ventricular strain and volume analyses through deep learning-based fully automatic segmentation based on radial long-axis reconstruction of short-axis cine magnetic resonance images.

Magma (New York, N.Y.)
OBJECTIVE: We propose a deep learning-based fully automatic right ventricle (RV) segmentation technique that targets radially reconstructed long-axis (RLA) images of the center of the RV region in routine short axis (SA) cardiovascular magnetic reson...

Meta multi-task nuclei segmentation with fewer training samples.

Medical image analysis
Cells/nuclei deliver massive information of microenvironment. An automatic nuclei segmentation approach can reduce pathologists' workload and allow precise of the microenvironment for biological and clinical researches. Existing deep learning models ...

Automatic recognition of micronucleus by combining attention mechanism and AlexNet.

BMC medical informatics and decision making
BACKGROUND: Micronucleus (MN) is an abnormal fragment in a human cell caused by disorders in the mechanism regulating chromosome segregation. It can be used as a biomarker for genotoxicity, tumor risk, and tumor malignancy. The in vitro micronucleus ...

Generative convolution layer for image generation.

Neural networks : the official journal of the International Neural Network Society
This paper introduces a novel convolution method, called generative convolution (GConv), which is simple yet effective for improving the generative adversarial network (GAN) performance. Unlike the standard convolution, GConv first selects useful ker...