Deep Learning Models for Semantic Multi-Modal Medical Image Segmentation

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Author(s): V. R. S. Mani (National Engineering College, Kovilpatti, India)
Copyright: 2023
Pages: 19
Source title: Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-7544-7.ch007

Keywords: Knowledge Management and Library Science / Medical Diagnosis and Practice / Medical Imaging / Medical Information Science Reference

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Abstract

In this chapter, the author paints a comprehensive picture of different deep learning models used in different multi-modal image segmentation tasks. This chapter is an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. Methods are classified according to the different types of multi-modal images and the corresponding types of convolution neural networks used in the segmentation task. The chapter starts with an introduction to CNN topology and describes various models like Hyper Dense Net, Organ Attention Net, UNet, VNet, Dilated Fully Convolutional Network, Transfer Learning, etc.

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Deep Learning Models for Semantic Multi-Modal Medical Image Segmentation

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