AIMC Topic: Machine Learning

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Small airway segmentation in thoracic computed tomography scans: a machine learning approach.

Physics in medicine and biology
Small airway obstruction is a main cause for chronic obstructive pulmonary disease (COPD). We propose a novel method based on machine learning to extract the airway system from a thoracic computed tomography (CT) scan. The emphasis of the proposed me...

Improved perfusion pattern score association with type 2 diabetes severity using machine learning pipeline: Pilot study.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Type 2 diabetes mellitus (T2DM) is associated with alterations in the blood-brain barrier, neuronal damage, and arterial stiffness, thus affecting cerebral metabolism and perfusion. There is a need to implement machine-learning methodolog...

Use of machine learning to predict early biochemical recurrence after robot-assisted prostatectomy.

BJU international
OBJECTIVES: To train and compare machine-learning algorithms with traditional regression analysis for the prediction of early biochemical recurrence after robot-assisted prostatectomy.

State representation learning for control: An overview.

Neural networks : the official journal of the International Neural Network Society
Representation learning algorithms are designed to learn abstract features that characterize data. State representation learning (SRL) focuses on a particular kind of representation learning where learned features are in low dimension, evolve through...

A New Approach on HCI Extracting Conscious Jaw Movements Based on EEG Signals Using Machine Learnings.

Journal of medical systems
Machine computer interfaces (MCI) are assistive technologies enabling paralyzed peoples to control and communicate their environments. This study aims to discover and represents a new approach on MCI using left/right motions of voluntary jaw movement...

A user-guided tool for semi-automated cerebral microbleed detection and volume segmentation: Evaluating vascular injury and data labelling for machine learning.

NeuroImage. Clinical
BACKGROUND AND PURPOSE: With extensive research efforts in place to address the clinical relevance of cerebral microbleeds (CMBs), there remains a need for fast and accurate methods to detect and quantify CMB burden. Although some computer-aided dete...

Machine learning as a new paradigm for characterizing localization and lateralization of neuropsychological test data in temporal lobe epilepsy.

Epilepsy & behavior : E&B
In this study, we employed a kernel support vector machine to predict epilepsy localization and lateralization for patients with a diagnosis of epilepsy (n = 228). We assessed the accuracy to which indices of verbal memory, visual memory, verbal flue...