Current trends in machine-learning methods applied to spectroscopic cancer diagnosis

2014 ◽  
Vol 59 ◽  
pp. 17-25 ◽  
Author(s):  
Martina Sattlecker ◽  
Nicholas Stone ◽  
Conrad Bessant
Keyword(s):  
Machine Learning ◽  
Cancer Diagnosis ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Current Trends

scholarly journals Machine Learning Methods for Histopathological Image Analysis: A Review

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 562
Author(s):  
Jonathan de Matos ◽  
Steve Ataky ◽  
Alceu de Souza Britto ◽  
Luiz Soares de Oliveira ◽  
Alessandro Lameiras Koerich
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Cancer Diagnosis ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Histopathological Image ◽  
Histopathological Images ◽  
Private Datasets ◽  
Aided Diagnosis ◽  
Histopathological Image Analysis

Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis. The analysis of such images is time and resource-consuming and very challenging even for experienced pathologists, resulting in inter-observer and intra-observer disagreements. One of the ways of accelerating such an analysis is to use computer-aided diagnosis (CAD) systems. This paper presents a review on machine learning methods for histopathological image analysis, including shallow and deep learning methods. We also cover the most common tasks in HI analysis, such as segmentation and feature extraction. Besides, we present a list of publicly available and private datasets that have been used in HI research.

Application of machine learning methods for automatic interpretation of open hole logging data

2020 ◽  
pp. 44-47
Author(s):  
M.A. Basyrov ◽  
◽  
A.V. Akinshin ◽  
I.R. Makhmutov ◽  
Yu.D. Kantemirov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Automatic Interpretation ◽  
Open Hole
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