AIMC Topic: Diagnostic Imaging

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Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework.

Scientific reports
Precision medicine approaches rely on obtaining precise knowledge of the true state of health of an individual patient, which results from a combination of their genetic risks and environmental exposures. This approach is currently limited by the lac...

Immunological Approach for Full NURBS Reconstruction of Outline Curves from Noisy Data Points in Medical Imaging.

IEEE/ACM transactions on computational biology and bioinformatics
Curve reconstruction from data points is an important issue for advanced medical imaging techniques, such as computer tomography (CT) and magnetic resonance imaging (MRI). The most powerful fitting functions for this purpose are the NURBS (non-unifor...

Deep Learning in Medical Image Analysis.

Annual review of biomedical engineering
This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the ...

Machine Learning for Medical Imaging.

Radiographics : a review publication of the Radiological Society of North America, Inc
Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine lear...

Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features.

Computational intelligence and neuroscience
As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CN...

Extreme learning machine based optimal embedding location finder for image steganography.

PloS one
In image steganography, determining the optimum location for embedding the secret message precisely with minimum distortion of the host medium remains a challenging issue. Yet, an effective approach for the selection of the best embedding location wi...

A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Highly accurate classification of biomedical images is an essential task in the clinical diagnosis of numerous medical diseases identified from those images. Traditional image classification methods combined with hand-craft...

An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification.

IEEE journal of biomedical and health informatics
The availability of medical imaging data from clinical archives, research literature, and clinical manuals, coupled with recent advances in computer vision offer the opportunity for image-based diagnosis, teaching, and biomedical research. However, t...