AIMC Topic: Machine Learning

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Highly accelerated multishot echo planar imaging through synergistic machine learning and joint reconstruction.

Magnetic resonance in medicine
PURPOSE: To introduce a combined machine learning (ML)- and physics-based image reconstruction framework that enables navigator-free, highly accelerated multishot echo planar imaging (msEPI) and demonstrate its application in high-resolution structur...

Dry eye is matched by increased intrasubject variability in tear osmolarity as confirmed by machine learning approach.

Archivos de la Sociedad Espanola de Oftalmologia
OBJECTIVE: Because of high variability, tear film osmolarity measures have been questioned in dry eye assessment. Understanding the origin of such variability would aid data interpretation. This study aims to evaluate osmolarity variability in a clin...

Prediction of antiepileptic drug treatment outcomes of patients with newly diagnosed epilepsy by machine learning.

Epilepsy & behavior : E&B
OBJECTIVE: The objective of this study was to build a supervised machine learning-based classifier, which can accurately predict the outcomes of antiepileptic drug (AED) treatment of patients with newly diagnosed epilepsy.

Type-2 Fuzzy PCA Approach in Extracting Salient Features for Molecular Cancer Diagnostics and Prognostics.

IEEE transactions on nanobioscience
Machine learning is becoming a powerful tool for cancer diagnosis and prognosis based on classification using high dimensional molecular data. However, extracting classification features from high-dimensional datasets remains a challenging problem. P...

Low-Rank Deep Convolutional Neural Network for Multitask Learning.

Computational intelligence and neuroscience
In this paper, we propose a novel multitask learning method based on the deep convolutional network. The proposed deep network has four convolutional layers, three max-pooling layers, and two parallel fully connected layers. To adjust the deep networ...

Predicting nationwide obesity from food sales using machine learning.

Health informatics journal
The obesity epidemic progresses everywhere across the globe, and implementing frequent nationwide surveys to measure the percentage of obese population is costly. Conversely, country-level food sales information can be accessed inexpensively through ...

Model-free prostate cancer segmentation from dynamic contrast-enhanced MRI with recurrent convolutional networks: A feasibility study.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) is a method of temporal imaging that is commonly used to aid in prostate cancer (PCa) diagnosis and staging. Typically, machine learning models designed for the segmentation and detecti...

A population health perspective on artificial intelligence.

Healthcare management forum
The burgeoning field of Artificial Intelligence (AI) has the potential to profoundly impact the public's health. Yet, to make the most of this opportunity, decision-makers must understand AI concepts. In this article, we describe approaches and field...

Prediction of the five-day biochemical oxygen demand and chemical oxygen demand in natural streams using machine learning methods.

Environmental monitoring and assessment
Rivers, as the most prominent component of water resources, have a key role to play in increasing the life expectancy of living creatures. The essential characteristics of water pollutants can be described by water quality indices (WQIs). Hence, a fe...

Single-cell approaches to cell competition: High-throughput imaging, machine learning and simulations.

Seminars in cancer biology
Cell competition is a quality control mechanism in tissues that results in the elimination of less fit cells. Over the past decade, the phenomenon of cell competition has been identified in many physiological and pathological contexts, driven either ...