Deep Learning Models for Biomedical Image Analysis

Deep Learning Models for Biomedical Image Analysis

Bo Ji (Nanjing Tech University Pujiang Institute, China), Wenlu Zhang (California State University – Long Beach, USA), Rongjian Li (KeyBank, USA) and Hao Ji (California State Polytechnic University – Pomona, USA)
Copyright: © 2019 |Pages: 21
DOI: 10.4018/978-1-5225-7467-5.ch005

Abstract

Biomedical image analysis has become critically important to the public health and welfare. However, analyzing biomedical images is time-consuming and labor-intensive, and has long been performed manually by highly trained human experts. As a result, there has been an increasing interest in applying machine learning to automate biomedical image analysis. Recent progress in deep learning research has catalyzed the development of machine learning in learning discriminative features from data with minimum human intervention. Many deep learning models have been designed and achieved superior performance in various data analysis applications. This chapter starts with the basic of deep learning models and some practical strategies for handling biomedical image applications with limited data. After that, case studies of deep feature extraction for gene expression pattern image annotations, imaging data completion for brain disease diagnosis, and segmentation of infant brain tissue images are discussed to demonstrate the effectiveness of deep learning in biomedical image analysis.
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The Basic Of Deep Learning Models

Deep learning is a subset of the machine learning algorithms derived from neural networks. In this section, we review the basic of neural networks and one particular deep learning architecture, convolutional neural network. Transfer learning and data augmentation are discussed for training deep learning models in scenarios with insufficient data.

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