Image Analysis and Machine Learning Techniques for Computer-aided Diagnosis

近年来，在医生的医疗诊断中，数字图像的使用出现了惊人的增长。图像存档和通信系统(PACS)因其具有低成本、高质量和灵活地在医疗专业人员之间共享患者数据等优点而被广泛应用于医疗保健领域。它还为使用计算机加快诊断速度和减少主观性开辟了可能性。计算机辅助诊断(CAD)系统已经在许多国家用于乳腺癌筛查，并有望扩展到许多其他类型的疾病的诊断。图像分析技术在开发CAD系统中发挥着关键作用。然而，不同的模式(例如，X射线、CT、MRI、病理学)、组织/器官类型和疾病，图像的特征明显不同。对于如此广泛的图像，开发图像分析和机器学习技术是一个巨大的挑战。这项拟议的研究将为计算机辅助诊断系统开发稳健的图像分析和机器学习技术，这些技术可以将原始成像数据(2D或3D)转换为临床决策。它将集中于困扰稳健医学图像分析技术发展的几个问题，例如(I)稳健的手工特征和图像模型，(Ii)用于医学图像分析的高效深度学习架构，(Iii)手动和深度特征的高效同时使用，以及(Iv)用于图像分析和机器学习系统的硬件架构。全球缺乏专科医生，诊断经常被推迟。这项拟议的研究将导致智能系统的开发，这些系统可以帮助医生进行快速准确的诊断。根据最近的一份报告，到2024年，全球医学图像分析软件的市场规模预计将达到45亿美元。因此，CAD系统的成功开发有望带来可申请专利的知识产权，并为加拿大经济增添活力。

A phenomenal growth in the use of digital images has been observed in recent times in medical diagnosis by physicians. Picture archiving and communications system (PACS) are being widely used in health care as it offers several advantages such as low cost, improved quality, and flexibility in sharing patient data among the health care professionals. It also opens up the possibility of using computers to speed up diagnosis and reduce subjectiveness. The computer-aided diagnosis (CAD) system are already being used for breast cancer screening in many countries, and is expected to be extended for diagnosis of many other kinds of diseases.The image analysis techniques play a key role in developing CAD systems. However, the characteristics of images vary significantly across different modalities (e.g., X-ray, CT, MRI, pathology), tissue/organ types, and diseases. It is a huge challenge to develop image analysis and machine learning techniques for such a wide variety of images. The proposed research will develop robust image analysis and machine learning techniques for computer-aided diagnosis systems that can translate raw imaging data (2D or 3D) into clinical decisions. It will focus on several issues plaguing the development of robust medical image analysis techniques, such as (i) robust handcrafted features and image models, (ii) efficient deep learning architectures for medical image analysis, (iii) efficient simultaneous usage of handcrafted and deep features, and (iv) hardware architecture for image analysis and machine learning systems. There is a global shortage of specialist doctors and diagnosis often gets delayed. The proposed research will lead to development of intelligent systems that can help doctors perform quick and accurate diagnosis. According to a recent report, the global medical image analysis software market size is expected to reach USD $4.5 billion by 2024. A successful development of CAD systems is therefore expected to result in patentable Intellectual Property, and add to the Canadian economy.