AIMC Topic: Machine Learning

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Fetal health status prediction based on maternal clinical history using machine learning techniques.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Congenital anomalies are seen at 1-3% of the population, probabilities of which are tried to be found out primarily through double, triple and quad tests during pregnancy. Also, ultrasonographical evaluations of fetuses enha...

A deep learning framework to discern and count microscopic nematode eggs.

Scientific reports
In order to identify and control the menace of destructive pests via microscopic image-based identification state-of-the art deep learning architecture is demonstrated on the parasitic worm, the soybean cyst nematode (SCN), Heterodera glycines. Soybe...

Fisher Discrimination Regularized Robust Coding Based on a Local Center for Tumor Classification.

Scientific reports
Tumor classification is crucial to the clinical diagnosis and proper treatment of cancers. In recent years, sparse representation-based classifier (SRC) has been proposed for tumor classification. The employed dictionary plays an important role in sp...

Predictive modeling for odor character of a chemical using machine learning combined with natural language processing.

PloS one
Recent studies on machine learning technology have reported successful performances in some visual and auditory recognition tasks, while little has been reported in the field of olfaction. In this paper we report computational methods to predict the ...

Exploiting layerwise convexity of rectifier networks with sign constrained weights.

Neural networks : the official journal of the International Neural Network Society
By introducing sign constraints on the weights, this paper proposes sign constrained rectifier networks (SCRNs), whose training can be solved efficiently by the well known majorization-minimization (MM) algorithms. We prove that the proposed two-hidd...

Classifying the molecular functions of Rab GTPases in membrane trafficking using deep convolutional neural networks.

Analytical biochemistry
Deep learning has been increasingly used to solve a number of problems with state-of-the-art performance in a wide variety of fields. In biology, deep learning can be applied to reduce feature extraction time and achieve high levels of performance. I...

Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers.

Medical physics
PURPOSE: Machine learning classification algorithms (classifiers) for prediction of treatment response are becoming more popular in radiotherapy literature. General Machine learning literature provides evidence in favor of some classifier families (r...

Information-Based Medicine in Glioma Patients: A Clinical Perspective.

Computational and mathematical methods in medicine
Glioma constitutes the most common type of primary brain tumor with a dismal survival, often measured in terms of months or years. The thin line between treatment effectiveness and patient harm underpins the importance of tailoring clinical managemen...

Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Artificial intelligence (AI) is emerging as a technology with the power to transform established industries, and with applications from automated manufacturing to advertising and facial recognition to fully autonomous transportation. Advances in each...