The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Semantic Medical Image Analysis: An Alternative to Cross-Domain Transfer Learning
|
|
Author(s): Joy Nkechinyere Olawuyi (Adeyemi College of Education, Nigeria), Bernard Ijesunor Akhigbe (Department of Computer Science & Engineering, Obafemi Awolowo University, Nigeria), Babajide Samuel Afolabi (Obafemi Awolowo University, Nigeria)and Attoh Okine (University of Delaware, USA)
Copyright: 2021
Pages: 19
Source title:
Advanced Concepts, Methods, and Applications in Semantic Computing
Source Author(s)/Editor(s): Olawande Daramola (Cape Peninsula University of Technology, South Africa)and Thomas Moser (St. Pölten University of Applied Sciences, Austria)
DOI: 10.4018/978-1-7998-6697-8.ch007
Purchase
|
Abstract
The recent advancement in imaging technology, together with the hierarchical feature representation capability of deep learning models, has led to the popularization of deep learning models. Thus, research tends towards the use of deep neural networks as against the hand-crafted machine learning algorithms for solving computational problems involving medical images analysis. This limitation has led to the use of features extracted from non-medical data for training models for medical image analysis, considered optimal for practical implementation in clinical setting because medical images contain semantic contents that are different from that of natural images. Therefore, there is need for an alternative to cross-domain feature-learning. Hence, this chapter discusses the possible ways of harnessing domain-specific features which have semantic contents for development of deep learning models.
Related Content
|
Reinaldo Padilha França, Ana Carolina Borges Monteiro, Rangel Arthur, Yuzo Iano.
© 2021.
21 pages.
|
|
Abdul Kader Saiod, Darelle van Greunen.
© 2021.
28 pages.
|
|
Aswini R., Padmapriya N..
© 2021.
22 pages.
|
|
Zubeida Khan, C. Maria Keet.
© 2021.
21 pages.
|
|
Neha Gupta, Rashmi Agrawal.
© 2021.
20 pages.
|
|
Kamalendu Pal.
© 2021.
14 pages.
|
|
Joy Nkechinyere Olawuyi, Bernard Ijesunor Akhigbe, Babajide Samuel Afolabi, Attoh Okine.
© 2021.
19 pages.
|
|
|