AIMC Topic: Machine Learning

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Cross Euclidean-to-Riemannian Metric Learning with Application to Face Recognition from Video.

IEEE transactions on pattern analysis and machine intelligence
Riemannian manifolds have been widely employed for video representations in visual classification tasks including video-based face recognition. The success mainly derives from learning a discriminant Riemannian metric which encodes the non-linear geo...

Looking for Alzheimer's Disease morphometric signatures using machine learning techniques.

Journal of neuroscience methods
BACKGROUND: We present our results in the International challenge for automated prediction of MCI from MRI data. We evaluate the performance of MRI-based neuromorphometrics features (nMF) in the classification of Healthy Controls (HC), Mild Cognitive...

Different protein-protein interface patterns predicted by different machine learning methods.

Scientific reports
Different types of protein-protein interactions make different protein-protein interface patterns. Different machine learning methods are suitable to deal with different types of data. Then, is it the same situation that different interface patterns ...

A machine learning approach to triaging patients with chronic obstructive pulmonary disease.

PloS one
COPD patients are burdened with a daily risk of acute exacerbation and loss of control, which could be mitigated by effective, on-demand decision support tools. In this study, we present a machine learning-based strategy for early detection of exacer...

Environmental metabolomics with data science for investigating ecosystem homeostasis.

Progress in nuclear magnetic resonance spectroscopy
A natural ecosystem can be viewed as the interconnections between complex metabolic reactions and environments. Humans, a part of these ecosystems, and their activities strongly affect the environments. To account for human effects within ecosystems,...

Machine learning, social learning and the governance of self-driving cars.

Social studies of science
Self-driving cars, a quintessentially 'smart' technology, are not born smart. The algorithms that control their movements are learning as the technology emerges. Self-driving cars represent a high-stakes test of the powers of machine learning, as wel...

Correlation Filters for Detection of Cellular Nuclei in Histopathology Images.

Journal of medical systems
UNLABELLED: Nuclei detection in histology images is an essential part of computer aided diagnosis of cancers and tumors. It is a challenging task due to diverse and complicated structures of cells. In this work, we present an automated technique for ...

Profiling Arthritis Pain with a Decision Tree.

Pain practice : the official journal of World Institute of Pain
BACKGROUND: Arthritis is the leading cause of work disability and contributes to lost productivity. Previous studies showed that various factors predict pain, but they were limited in sample size and scope from a data analytics perspective.

Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: Our purpose was to use deep learning for the automated detection of age-related macular degeneration (AMD) in spectral domain optical coherence tomography (SD-OCT).

Clinical decision support of radiotherapy treatment planning: A data-driven machine learning strategy for patient-specific dosimetric decision making.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Clinical decision support systems are a growing class of tools with the potential to impact healthcare. This study investigates the construction of a decision support system through which clinicians can efficiently identify wh...