Deep Learning for Medical Image Analysis

Deep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas.

Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis.

• Covers common research problems in medical image analysis and their challenges
• Describes deep learning methods and the theories behind approaches for medical image analysis
• Teaches how algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc.
• Includes a Foreword written by Nicholas Ayache

深度學習為醫學影像分析問題提供了令人興奮的解決方案，被視為未來應用的關鍵方法。本書清晰地介紹了神經網絡和深度學習概念的原理和方法，展示了將深度學習作為核心組件的算法如何應用於醫學影像檢測、分割和配準以及計算機輔助分析，涵蓋了各種應用領域。

《深度學習在醫學影像分析中的應用》是醫學影像分析領域的學術和工業研究人員以及修讀機器學習、計算機視覺和醫學影像計算和分析課程的研究生的學習資源。

本書內容包括：
- 涵蓋醫學影像分析中常見的研究問題及其挑戰
- 描述深度學習方法和醫學影像分析方法背後的理論
- 教授如何將算法應用於各種應用領域，包括胸部X光、乳腺CAD、肺部和胸部、顯微鏡和病理學等
- 包含由Nicholas Ayache撰寫的前言