AIMC Topic: Image Processing, Computer-Assisted

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Quality Control for High-Throughput Imaging Experiments Using Machine Learning in Cellprofiler.

Methods in molecular biology (Clifton, N.J.)
Robust high-content screening of visual cellular phenotypes has been enabled by automated microscopy and quantitative image analysis. The identification and removal of common image-based aberrations is critical to the screening workflow. Out-of-focus...

Intravoxel Incoherent Motion: Model-Free Determination of Tissue Type in Abdominal Organs Using Machine Learning.

Investigative radiology
PURPOSE: For diffusion data sets including low and high b-values, the intravoxel incoherent motion model is commonly applied to characterize tissue. The aim of the present study was to show that machine learning allows a model-free approach to determ...

Computer-Aided Diagnosis of Lung Nodules in Computed Tomography by Using Phylogenetic Diversity, Genetic Algorithm, and SVM.

Journal of digital imaging
Lung cancer is pointed as the major cause of death among patients with cancer throughout the world. This work is intended to develop a methodology for diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resour...

Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms.

GigaScience
The study of phenomes or phenomics has been a central part of biology. The field of automatic phenotype acquisition technologies based on images has seen an important advance in the last years. As with other high-throughput technologies, it addresses...

CASAnova: a multiclass support vector machine model for the classification of human sperm motility patterns.

Biology of reproduction
The ability to accurately monitor alterations in sperm motility is paramount to understanding multiple genetic and biochemical perturbations impacting normal fertilization. Computer-aided sperm analysis (CASA) of human sperm typically reports motile ...

Machine Learning Applications to Resting-State Functional MR Imaging Analysis.

Neuroimaging clinics of North America
Machine learning is one of the most exciting and rapidly expanding fields within computer science. Academic and commercial research entities are investing in machine learning methods, especially in personalized medicine via patient-level classificati...

Combining deep learning and coherent anti-Stokes Raman scattering imaging for automated differential diagnosis of lung cancer.

Journal of biomedical optics
Lung cancer is the most prevalent type of cancer and the leading cause of cancer-related deaths worldwide. Coherent anti-Stokes Raman scattering (CARS) is capable of providing cellular-level images and resolving pathologically related features on hum...

A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction.

Medical physics
PURPOSE: Due to the potential risk of inducing cancer, radiation exposure by X-ray CT devices should be reduced for routine patient scanning. However, in low-dose X-ray CT, severe artifacts typically occur due to photon starvation, beam hardening, an...

Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies.

GigaScience
Genetic analyses of plant root systems require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a...

Trajectory Predictor by Using Recurrent Neural Networks in Visual Tracking.

IEEE transactions on cybernetics
Motion models have been proved to be a crucial part in the visual tracking process. In recent trackers, particle filter and sliding windows-based motion models have been widely used. Treating motion models as a sequence prediction problem, we can est...