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• W3124987322 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." @default.
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• W3124987322 date "2021-01-01" @default.
• W3124987322 modified "2023-09-28" @default.
• W3124987322 title "Semantic Medical Image Analysis" @default.
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• W3124987322 doi "https://doi.org/10.4018/978-1-7998-6697-8.ch007" @default.
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