AIMC Topic: Algorithms

Clear Filters Showing 27701 to 27710 of 28713 articles

Applying Densely Connected Convolutional Neural Networks for Staging Osteoarthritis Severity from Plain Radiographs.

Journal of digital imaging
Osteoarthritis (OA) classification in the knee is most commonly done with radiographs using the 0-4 Kellgren Lawrence (KL) grading system where 0 is normal, 1 shows doubtful signs of OA, 2 is mild OA, 3 is moderate OA, and 4 is severe OA. KL grading ...

Restoration of Full Data from Sparse Data in Low-Dose Chest Digital Tomosynthesis Using Deep Convolutional Neural Networks.

Journal of digital imaging
Chest digital tomosynthesis (CDT) provides more limited image information required for diagnosis when compared to computed tomography. Moreover, the radiation dose received by patients is higher in CDT than in chest radiography. Thus, CDT has not bee...

[Automatic Segmentation of Anatomical Areas in X-ray Images Based on Fully Convolutional Networks].

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
OBJECTIVE: Medical image segmentation is a key step in medical image processing. An architecture of fully convolutional networks was proposed to realize automatic segmentation of anatomical areas in X-ray images.

Gradient and Hamiltonian coupled systems on undirected networks.

Mathematical biosciences and engineering : MBE
Many real world applications are modelled by coupled systems on undirected networks. Two striking classes of such systems are the gradient and the Hamiltonian systems. In fact, within these two classes, coupled systems are admissible only by the undi...

Systematic analysis and prediction of type IV secreted effector proteins by machine learning approaches.

Briefings in bioinformatics
In the course of infecting their hosts, pathogenic bacteria secrete numerous effectors, namely, bacterial proteins that pervert host cell biology. Many Gram-negative bacteria, including context-dependent human pathogens, use a type IV secretion syste...

Effect of normalization methods on the performance of supervised learning algorithms applied to HTSeq-FPKM-UQ data sets: 7SK RNA expression as a predictor of survival in patients with colon adenocarcinoma.

Briefings in bioinformatics
MOTIVATION: One of the main challenges in machine learning (ML) is choosing an appropriate normalization method. Here, we examine the effect of various normalization methods on analyzing FPKM upper quartile (FPKM-UQ) RNA sequencing data sets. We coll...

Enhanced prediction of RNA solvent accessibility with long short-term memory neural networks and improved sequence profiles.

Bioinformatics (Oxford, England)
MOTIVATION: The de novo prediction of RNA tertiary structure remains a grand challenge. Predicted RNA solvent accessibility provides an opportunity to address this challenge. To the best of our knowledge, there is only one method (RNAsnap) available ...

Pixel super-resolution for lens-free holographic microscopy using deep learning neural networks.

Optics express
Lens-free holographic microscopy (LFHM) provides a cost-effective tool for large field-of-view imaging in various biomedical applications. However, due to the unit optical magnification, its spatial resolution is limited by the pixel size of the imag...

A Deep Learning-Based Decision Support Tool for Precision Risk Assessment of Breast Cancer.

JCO clinical cancer informatics
PURPOSE: The Breast Imaging Reporting and Data System (BI-RADS) lexicon was developed to standardize mammographic reporting to assess cancer risk and facilitate the decision to biopsy. Because of substantial interobserver variability in the applicati...

Burn wound classification model using spatial frequency-domain imaging and machine learning.

Journal of biomedical optics
Accurate assessment of burn severity is critical for wound care and the course of treatment. Delays in classification translate to delays in burn management, increasing the risk of scarring and infection. To this end, numerous imaging techniques have...