NeXt for neuro-radiosurgery: A fully automatic approach for necrosis extraction in brain tumor MRI using an unsupervised machine learning technique

2017 ◽  
Vol 28 (1) ◽  
pp. 21-37 ◽  
Author(s):  
Leonardo Rundo ◽  
Carmelo Militello ◽  
Andrea Tangherloni ◽  
Giorgio Russo ◽  
Salvatore Vitabile ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Machine Learning Technique ◽  
Unsupervised Machine Learning ◽  
Learning Technique ◽  
Fully Automatic ◽  
Tumor Mri

Evaluation of caridean ecological distribution in the Ubatuba region, southeastern Brazilian coast using unsupervised machine learning technique

2021 ◽  
Author(s):  
Alexandre Oliveira Marques ◽  
Aline Nonato Sousa ◽  
Veronica Pereira Bernardes ◽  
Camila Hipolito Bernardo ◽  
Danielle Monique Reis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brazilian Coast ◽  
Ecological Distribution ◽  
Machine Learning Technique ◽  
Unsupervised Machine Learning ◽  
Learning Technique

scholarly journals Utilizing Unsupervised Machine Learning in BSM Physics Searches At The LHC

2020 ◽  
Vol 245 ◽  
pp. 06021
Author(s):  
Adam Leinweber ◽  
Martin White
Keyword(s):  
Machine Learning ◽  
Large Hadron Collider ◽  
Hadron Collider ◽  
New Physics ◽  
Machine Learning Technique ◽  
Unsupervised Machine Learning ◽  
Learning Technique ◽  
Bsm Physics ◽  
Supersymmetric Particles ◽  
One Machine

Recent searches for supersymmetric particles at the Large Hadron Collider have been unsuccessful in detecting any BSM physics. This is partially because the exact masses of supersymmetric particles are not known, and as such, searching for them is very difficult. The method broadly used in searching for new physics requires one to optimise on the signal being searched for, potentially suppressing sensitivity to new physics which may actually be present that does not resemble the chosen signal. The problem with this approach is that, in order to detect something with this method, one must already know what to look for. I will showcase one machine-learning technique that can be used to define a “signal-agnostic” search. This is a search that does not make any assumptions about the signal being searched for, allowing it to detect a signal in a more general way. This method is applied to simulated BSM physics data and the results are explored.

scholarly journals Image Based Group Classifier for Brain Tumor Detection Using Machine Learning Technique

2020 ◽  
Vol 37 (5) ◽  
pp. 865-871
Author(s):  
Putta Rama Krishnaveni ◽  
Gattim Naveen Kishore
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
The United States ◽  
Tumor Detection ◽  
Tumor Type ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
The Brain

In view of insights of the Central Brain Tumor Registry of the United States (CBTRUS), brain tumor is one of the main sources of disease related deaths in the World. It is the subsequent reason for tumor related deaths in adults under the age 20-39. Magnetic Resonance Imaging (MRI) is assuming a significant job in the examination of neuroscience for contemplating brain images. The investigation of brain MRI Images is useful in brain tumor analysis process. Features will be extricated and selected from the segmented pictures and afterward grouped by utilizing the classification procedures to analyze whether the patient is ordinary (having no tumor) or irregular (having tumor). One of the most dangerous cancers is brain tumor or cancer which affects the human body's main nervous system. Infection that can affect is very sensitive to the brain. Two types of brain tumors are present. The tumor may be categorized as benign and malignant. The benign tumor represents a change in the shape and structure of the cells, but cannot contaminate or spread to other cells in the brain. The malignant tumor can spread and grow if not carefully treated and removed. The detection of brain tumors is a difficult and sensitive task involving the classifier's experience. In the proposed work a Group based Classifier for Brain Tumor Recognition (GbCBTD) is introduced for the efficient segmentation of MRI images and for identification of tumor. The use of Convolutional Neural Network (CNN) system to classify the brain tumor type is presented in this work. Relevant features are extracted from images and by using CNN with machine learning technique, tumor can be recognized. CNN can reduce the cost and increase the performance of brain tumor detection. The proposed work is compared to the traditional methods and the results show that the proposed method is effective in detecting tumors.

scholarly journals uniForest: an unsupervised machine learning technique to detect outliers and restrict variance in microbiome studies

2021 ◽  
Author(s):  
Robert J. Leigh ◽  
Richard A. Murphy ◽  
Fiona Walsh
Keyword(s):  
Machine Learning ◽  
Variance Reduction ◽  
Principal Component ◽  
Alpha Diversity ◽  
Anatomical Site ◽  
Desktop Computer ◽  
Machine Learning Technique ◽  
Healthy Human ◽  
Unsupervised Machine Learning ◽  
Learning Technique

Isolation Forests is an unsupervised machine learning technique for detecting outliers in continuous datasets that does not require an underlying equivariant or Gaussian distribution and is suitable for use on small datasets. While this procedure is widely used across quantitative fields, to our knowledge, this is the first attempt to solely assess its use for microbiome datasets. Here we present uniForest, an interactive Python notebook (which can be run from any desktop computer using the Google Colaboratory web service) for the processing of microbiome outliers. We used uniForest to apply Isolation Forests to the Healthy Human Microbiome project dataset and imputed outliers with the mean of the remaining inliers to maintain sample size and assessed its prowess in variance reduction in both community structure and derived ecological statistics (alpha-diversity). We also assessed its functionality in anatomical site differentiation (pre- and postprocessing) using principal component analysis, dissimilarity matrices, and ANOSIM. We observed a minimum variance reduction of 81.17% across the entire dataset and in alpha diversity at the Phylum level. Application of Isolation Forests also separated the dataset to an extremely high specificity, reducing variance within taxa samples by a minimum of 81.33%. It is evident that Isolation Forests are a potent tool in restricting the effect of variance in microbiome analysis and has potential for broad application in studies where high levels of microbiome variance is expected. This software allows for clean analyses of otherwise noisy datasets.

scholarly journals A survey on detection of brain tumor using computer vision and machine learning technique

2021 ◽  
Vol 7 (3) ◽  
pp. 84-88
Author(s):  
Pranav Shetty ◽  
Suraj Singh ◽  
Rasvi Jambhulkar ◽  
Kajal Sheth ◽  
Deepali Ujalambkar
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Brain Tumor ◽  
Machine Learning Technique ◽  
Learning Technique
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