Multiple Kernel Learning Approach For Medical Image Analysis

Computer aided diagnosis is gradually making its way into the domain of medical research and clinical diagnosis. With Field of radiology and diagnostic imaging producing petabytes of image data. Machine learning tools, particularly kernel based algorithms seem to be an obvious choice to process and...

Full description

Saved in:
Bibliographic Details
Published inbioRxiv
Main Authors Wani, Nisar, Raza, Khalid
Format Paper
LanguageEnglish
Published Cold Spring Harbor Cold Spring Harbor Laboratory Press 29.03.2017
Cold Spring Harbor Laboratory
Edition1.1
Subjects
Algorithms
Artificial intelligence
Bioinformatics
Diagnosis
Image processing
Learning algorithms
Medical research
kernel learning
image descriptors
medical image analysis
MKL
Online AccessGet full text
ISSN2692-8205
2692-8205
DOI10.1101/121509

Cover

More Information
Summary:Computer aided diagnosis is gradually making its way into the domain of medical research and clinical diagnosis. With Field of radiology and diagnostic imaging producing petabytes of image data. Machine learning tools, particularly kernel based algorithms seem to be an obvious choice to process and analyze this high dimensional and heterogeneous data. In this chapter, after presenting a brief description about nature of medical images, image features and basics in machine learning and kernel methods, we present the application of multiple kernel learning algorithms for medical image analysis.
Bibliography:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
ISSN:2692-8205
2692-8205
DOI:10.1101/121509