Semantic Medical Image Analysis
| Autor: | Joy Nkechinyere Olawuyi, Babajide Afolabi, Bernard Ijesunor Akhigbe, Attoh Okine |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry Pattern recognition 02 engineering and technology 030218 nuclear medicine & medical imaging Image (mathematics) Domain (software engineering) 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Transfer of learning business |
| DOI: | 10.4018/978-1-7998-6697-8.ch007 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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