AIMC Topic: Image Processing, Computer-Assisted

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A Method Based on Artificial Intelligence To Fully Automatize The Evaluation of Bovine Blastocyst Images.

Scientific reports
Morphological analysis is the standard method of assessing embryo quality; however, its inherent subjectivity tends to generate discrepancies among evaluators. Using genetic algorithms and artificial neural networks (ANNs), we developed a new method ...

Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images.

Journal of healthcare engineering
Lung cancer is the most common cancer that cannot be ignored and cause death with late health care. Currently, CT can be used to help doctors detect the lung cancer in the early stages. In many cases, the diagnosis of identifying the lung cancer depe...

Decoding naturalistic experiences from human brain activity via distributed representations of words.

NeuroImage
Natural visual scenes induce rich perceptual experiences that are highly diverse from scene to scene and from person to person. Here, we propose a new framework for decoding such experiences using a distributed representation of words. We used functi...

Assessment of beer quality based on foamability and chemical composition using computer vision algorithms, near infrared spectroscopy and machine learning algorithms.

Journal of the science of food and agriculture
BACKGROUND: Beer quality is mainly defined by its colour, foamability and foam stability, which are influenced by the chemical composition of the product such as proteins, carbohydrates, pH and alcohol. Traditional methods to assess specific chemical...

A patch-based convolutional neural network for remote sensing image classification.

Neural networks : the official journal of the International Neural Network Society
Availability of accurate land cover information over large areas is essential to the global environment sustainability; digital classification using medium-resolution remote sensing data would provide an effective method to generate the required land...

Fast DCNN based on FWT, intelligent dropout and layer skipping for image retrieval.

Neural networks : the official journal of the International Neural Network Society
Deep Convolutional Neural Network (DCNN) can be marked as a powerful tool for object and image classification and retrieval. However, the training stage of such networks is highly consuming in terms of storage space and time. Also, the optimization i...

Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning.

PloS one
Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural hete...

Pre-trained convolutional neural networks as feature extractors for tuberculosis detection.

Computers in biology and medicine
It is estimated that in 2015, approximately 1.8 million people infected by tuberculosis died, most of them in developing countries. Many of those deaths could have been prevented if the disease had been detected at an earlier stage, but the most adva...

Holographic deep learning for rapid optical screening of anthrax spores.

Science advances
Establishing early warning systems for anthrax attacks is crucial in biodefense. Despite numerous studies for decades, the limited sensitivity of conventional biochemical methods essentially requires preprocessing steps and thus has limitations to be...

SiNC: Saliency-injected neural codes for representation and efficient retrieval of medical radiographs.

PloS one
Medical image collections contain a wealth of information which can assist radiologists and medical experts in diagnosis and disease detection for making well-informed decisions. However, this objective can only be realized if efficient access is pro...