COMPUTATIONAL INTELLIGENCE & IMAGE PROCESS MEDICAL APPL

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.

Contents:

• 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 Doğanay, 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 Doğanay, 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 Krzyżak 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)

Readership: Researchers, professionals, academics, and graduate students in artificial intelligence and machine learning.