Machine Learning for Tomographic Imaging. Professor Ge Wang

neural network or the dictionary learning method, is capable of extracting the prior information and representing it sparsely. The building block of a neural network is an artificial neuron which takes multiple variables as the input, computes an inner product between the input vector and a weighting vector, and thresholds the inner product in some way as the output of the neuron.

      Furthermore, the multi-layer neural mechanism exists in HVS, which inspires the development of multi-layer neural networks, which is commonly known as deep learning. Along this direction, a set of non-linear machine learning algorithms were developed for modeling complex data representations. It is the multi-layer artificial neural architecture that allows the learning of high-level representations of data through multi-scale analysis from low-level primitives to semantic features. It is comforting that this type of multi-layer neural networks resembles the multi-layer mechanism in the HVS. In the next chapter, we will cover the basics of artificial neural networks.

References

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      IOP Publishing

      Machine Learning for Tomographic Imaging

      Ge Wang, Yi Zhang, Xiaojing Ye and Xuanqin Mou

      Chapter 3

      Artificial neural networks

      3.1 Basic concepts

      In chapter 2, we have introduced tomographic reconstruction based on a learned structural dictionary in which the prior information of low-level image features is expressed as atoms, which are over-complete basis functions. This prior information is actually a result of image information extraction. Indeed, it is an essential task to find an efficient measure to express the information for various images contents. In the development of deep learning techniques, it has become a common belief now that multi-layer neural networks extract image information from different semantic levels, thereby representing image features effectively and efficiently, which is consistent with the principle of the human vision system (HVS) perceiving natural images. Therefore, in this chapter we will focus on the basic knowledge of artificial neural networks, providing the foundation for feature representation and reconstruction of medical images using deep neural networks.

3.1.1 Biological neural network

      Artificial neural networks originated from mimicking biological neural systems. It is necessary to understand the connection between the artificial neural network and the biological neural network before one is introduced to deep learning.

      The hierarchical structure of the HVS is shown in figure 1.2. In the HVS, features are extracted layer by layer. As described in chapter 1, in the ‘what’ pathway, the V1/V2 area is mainly sensitive to edges and lines, the V4 area senses object shapes, and finally the IT region completes the object recognition. In this process, the receptive field is constantly increasing in size, and the extracted features are increasingly more complex.

      To a large degree, the artificial neural network attempts to duplicate the biological neural network from the perspective of information processing. As a result, an artificial neural network serves as a simple mathematical model, and different networks are defined by different interconnections among various numbers of artificial neurons. A neural network is a computational framework, including a large number of neurons as basic computing units connected to each other with varying