Image Analysis and Machine Learning Techniques for Computer-aided Diagnosis

近年来，在医生的医学诊断中，数字图像的使用有了显著的增长。图片存档和通信系统（PACS）在医疗保健中得到广泛应用，因为它具有成本低、质量高和在医疗保健专业人员之间共享患者数据的灵活性等优点。它还开启了使用计算机加速诊断和减少主观性的可能性。计算机辅助诊断（CAD）系统已经在许多国家用于乳腺癌筛查，预计将扩展到许多其他类型疾病的诊断。图像分析技术在CAD系统的开发中起着关键作用。然而，不同形态（如x射线、CT、MRI、病理）、组织/器官类型和疾病的图像特征差异很大。为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。拟议的研究将为计算机辅助诊断系统开发强大的图像分析和机器学习技术，可以将原始成像数据（2D或3D）转化为临床决策。它将重点关注困扰鲁棒医学图像分析技术发展的几个问题，例如(i)鲁棒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）有效同时使用手工和深度特征，以及（iv）用于图像分析和机器学习系统的硬件架构。全球专科医生短缺，诊断常常被延误。拟议中的研究将导致智能系统的发展，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获得可申请专利的知识产权，并促进加拿大经济的发展。

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.