7,402 research outputs found
Artificial neural network-statistical approach for PET volume analysis and classification
Copyright © 2012 The Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.This article has been made available through the Brunel Open Access Publishing Fund.The increasing number of imaging studies and the prevailing application of positron emission tomography (PET) in clinical oncology have led to a real need for efficient PET volume handling and the development of new volume analysis approaches to aid the clinicians in the clinical diagnosis, planning of treatment, and assessment of response to therapy. A novel automated system for oncological PET volume analysis is proposed in this work. The proposed intelligent system deploys two types of artificial neural networks (ANNs) for classifying PET volumes. The first methodology is a competitive neural network (CNN), whereas the second one is based on learning vector quantisation neural network (LVQNN). Furthermore, Bayesian information criterion (BIC) is used in this system to assess the optimal number of classes for each PET data set and assist the ANN blocks to achieve accurate analysis by providing the best number of classes. The system evaluation was carried out using experimental phantom studies (NEMA IEC image quality body phantom), simulated PET studies using the Zubal phantom, and clinical studies representative of nonsmall cell lung cancer and pharyngolaryngeal squamous cell carcinoma. The proposed analysis methodology of clinical oncological PET data has shown promising results and can successfully classify and quantify malignant lesions.This study was supported by the Swiss National Science Foundation under Grant SNSF 31003A-125246, Geneva Cancer League, and the Indo Swiss Joint Research Programme ISJRP 138866. This article is made available through the Brunel Open Access Publishing Fund
Image Segmentation and Classification of Marine Organisms
To automate the arduous task of identifying and classifying images through their domain expertise, pioneers in the field of machine learning and computer vision invented many algorithms and pre-processing techniques. The process of classification is flexible with many user and domain specific alterations. These techniques are now being used to classify marine organisms to study and monitor their populations. Despite advancements in the field of programming languages and machine learning, image segmentation and classification for unlabeled data still needs improvement. The purpose of this project is to explore the various pre-processing techniques and classification algorithms that help cluster and classify images and hence choose the best parameters for identifying the various marine species present in an image
The applications of deep neural networks to sdBV classification
With several new large-scale surveys on the horizon, including LSST, TESS,
ZTF, and Evryscope, faster and more accurate analysis methods will be required
to adequately process the enormous amount of data produced. Deep learning, used
in industry for years now, allows for advanced feature detection in minimally
prepared datasets at very high speeds; however, despite the advantages of this
method, its application to astrophysics has not yet been extensively explored.
This dearth may be due to a lack of training data available to researchers.
Here we generate synthetic data loosely mimicking the properties of acoustic
mode pulsating stars and we show that two separate paradigms of deep learning -
the Artificial Neural Network And the Convolutional Neural Network - can both
be used to classify this synthetic data effectively. And that additionally this
classification can be performed at relatively high levels of accuracy with
minimal time spent adjusting network hyperparameters.Comment: 12 pages, 10 figures, originally presented at sdOB
Denoising Adversarial Autoencoders: Classifying Skin Lesions Using Limited Labelled Training Data
We propose a novel deep learning model for classifying medical images in the
setting where there is a large amount of unlabelled medical data available, but
labelled data is in limited supply. We consider the specific case of
classifying skin lesions as either malignant or benign. In this setting, the
proposed approach -- the semi-supervised, denoising adversarial autoencoder --
is able to utilise vast amounts of unlabelled data to learn a representation
for skin lesions, and small amounts of labelled data to assign class labels
based on the learned representation. We analyse the contributions of both the
adversarial and denoising components of the model and find that the combination
yields superior classification performance in the setting of limited labelled
training data.Comment: Under consideration for the IET Computer Vision Journal special issue
on "Computer Vision in Cancer Data Analysis
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