Computational Intelligence and Image Processing in Medical Applications

Computational Intelligence and Image Processing in Medical Applications（Series in Computer Vision）

计算智能和图像处理在医学中的应用
生物医学工程学

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作者

出版社

World Scientific Publishing

出版时间

2022年05月30日

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ＩＳＢＮ

9789811257445

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420 pp

语种

英文

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图书简介

In recent years, there have been significant progress in computational intelligence and image processing with machine learning and deep learning as important components of modern artificial intelligence. All these progresses face challenges in dealing with Covid-19 pandemic for detection and treatment.This comprehensive compendium provides not only updated advances of computational intelligence and image processing in the detection and treatment of Covid-19, but also other medical applications such as in cancer detection and cardiovascular diseases, etc. More traditional approaches such as 2D segmentation and 3D reconstruction are included.The useful reference text is an updated version of the edited title, Computer Vision in Medical Imaging (World Scientific, 2014) and its companion volume, Frontiers of Medical Imaging (World Scientific, 2015). The book is written for engineers, scientists and the medical community to meet the increased challenges in medical applications.

Introduction: An Introduction to Computational Intelligence and Image Processing (C H Chen); Intelligent Behavioral Trajectory Pattern Recognition for Longitudinal Trials (Julia Hua Fang and Honggang Wang); Some Learning Strategies for Medical Image Data (Subhashis Banerjee, Sanhita Basu, and Sushmita Mitra); Covid-19 Detection: On Cost-Sensitive Calibrated Model Uncertainty and Interpretability in Deep Learning for Covid-19 Detection (Biraja Ghoshal and Allan Tucker); A Comparative Study on Segmenting the Infectious Lung Area on CT Scans of Covid-19 Patients (Fikret Efe Doganay, Oyku Sahin, Sedat Ozer and C H Chen); Medical Imaging: Medical Images Segmentation: How Raters’ Experience May Affect the Quality of Reference (Silvana Dellepiane and Marco Trombini); Machine Learning Approach for Quantification of Neuropathy Using Confocal Microscopy Images of Cornea (Uvais Qidwai and Tooba Salahuddin); Blood Smear Analyses using Deep Learning: Current Challenges and Future Directions (Rabiah Al-Qudah and Ching Y Suen); 3D Brain Tumor Segmentation with Deep Learning Methods (Oyku Sahin, Fikret Efe Doganay, Sedat Ozer and C H Chen); Deep Learning Techniques for 2D and 3D Segmentation of Diseased Regions in an Ultrasound Image (Kyung Lee, Haeyun Lee, and Jae Youn Hwang); Machine Learning Enabled Quantitative Ultrasound Techniques for Tissue Differentiation (Shufan Yang); A New Feature Extraction Approach for Segmentation of IVUS Images (Adithya Gangidi and C H Chen); AI-Enabled ECG and EEG Signal Analysis Methods (Xuhui (Tracy) Chen); 3D Tomosynthesis to Detect Breast Cancer (Reprinted from the 5th Edition of Handbook of PRCV (Pattern Recognition and Computer Vision) (Y Lu, et al. ); Emerging Topics: Deep Learning for Dermatologist-Level Detection of Suspicious Pigmented Lesions in Wide-Field Images (Luis R Soenksen); Efficient Deep Learning on Embedded Systems in Medical Application (Xianju Wang); Application of Portable 3D Ultrasound Scanning Based on Lidar Assisted Positioning (Wen Mao Hsu, Po-Wen Kao, and Shelley Zhang)

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