计算智能和图像处理在医学中的应用 Computational Intelligence And Image Processing In Medical Applications
基本信息
Format Hardback | 336 pages
Dimensions 169.93 x 244.09 x 19.05mm | 734.82g
Publication date 08 Jul 2022
Publisher World Scientific Publishing Co Pte Ltd
ISBN10 9811257442
ISBN13 9789811257445
页面参数仅供参考，具体以实物为准
书籍简介
近年来，计算智能和图像处理方面取得了重大进展，机器学习和深度学习成为现代人工智能的重要组成部分。所有这些进展在应对Covid-19大流行病的检测和治疗方面面临挑战。
这本综合汇编不仅提供了计算智能和图像处理在检测和治疗Covid-19方面的新进展，而且还提供了其他医学应用，如在癌症检测和心血管疾病等方面。更多的传统方法，如二维分割和三维重建也包括在内。
这本有用的参考书是编辑的标题《医学成像中的计算机视觉》（World Scientific，2014）及其配套卷《医学成像前沿》（World Scientific，2015）的更新版本。该书是为工程师、科学家和医学界编写的，以应对医学应用中越来越多的挑战。
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.
作者简介
Edited By: C H Chen (University of Massachusetts Dartmouth, USA)
目录
Introduction:
An Introduction to Computational Intelligence and Image Processing (C H Chen)
Intelligent Behavioral Trajectory Pattern Recognition for Longitudinal Trials (Hua Fang and Honggang Wang)
Covid-19 Detection:
Segmentation of COVID-19 Infected Lung Area in CT Scans with Deep Algorithms (Oyku Sahin, Fikret Efe Do?anay, Sedat Ozer and Chi Hau Chen)
Active Learning for Medical Image Classification (Xianju Wang)
Medical Imaging:
Medical Image Segmentation: How Raters' Experience May Affect the Quality of Reference Information (Marco Trombini, Daniel Gut, Zbis law Tabor and Silvana Dellepiane)
Machine Learning Approach for Quantification of Neuropathy Using Confocal Microscopy Images of the Human Cornea (Tooba Salahuddin and Uvais Qidwai)
Peripheral Blood Smear Analysis Using Deep Learning: Current Challenges and Future Directions (Rabiah Al–qudah and Ching Y Suen)
Deep Learning based 3D Brain Tumor Segmentation with Multispectral MRI (Fikret Efe Do?anay, Oyku Sahin, Sedat Ozer and Chi Hau Chen)
Deep Learning Techniques for Ultrasound Image Enhancement and Segmentation (Kyungsu Lee, Haeyun Lee and Jae Youn Hwang)
A Fast Inference Framework for Medical Image Semantic Segmentation Tasks Using Deep Learning Framework (Shufan Yang and Yongjie Li)
A New Feature Extraction Approach for Segmentation of Intravascular Ultrasound Images (Adithya G Gangidi and Chi Hau Chen)
Deep Multi-task Learning Approach for Bioelectrical Signal Analysis (Xuhui Chen, Pusheng Ren and Jishu Medhi)
3D Tomosynthesis to Detect Breast Cancer (Yanbin Lu, Mina Yousefi, John Ellenberger, Richard H Moore, Daniel B Kopans, Adam Krzy?ak and Ching Y Suen)
Emerging Topics:
Using Deep Learning for Dermatologist-Level Detection of Ugly-Duckling and Suspicious Pigmented Skin Lesions from Wide-Field Images (Luis R Soenksen)
Deep Learning in Medical Imaging and Its Challenges (Xianju Wang)
