Image Analysis and Machine Learning Techniques for Computer-aided Diagnosis

用于计算机辅助诊断的图像分析和机器学习技术

• 批准号: RGPIN-2020-05873
• 负责人: Mandal, Mrinal
• 金额: $ 2.04万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752188
• 关键词: Image; Analysis; Machine; Learning; Techniques

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.

近年来，在医生的医学诊断中，已经观察到数字图像的使用的显著增长。图像存档和通信系统（PACS）正被广泛应用于医疗保健，因为它提供了几个优点，如低成本，提高质量，并在医疗保健专业人员之间共享患者数据的灵活性。它还开辟了利用计算机加快诊断速度和减少主观性的可能性。计算机辅助诊断（Computer Aided Diagnosis，CAD）系统已在许多国家用于乳腺癌筛查，并有望推广到其他疾病的诊断，图像分析技术在CAD系统的开发中起着关键作用。然而，图像的特性在不同模态之间显著变化（例如，X射线、CT、MRI、病理学）、组织/器官类型和疾病。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，这些系统可以将原始成像数据（2D或3D）转化为临床决策。它将专注于开发强大的医学图像分析技术的几个问题，例如（i）强大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）有效地同时使用手工和深度特征，以及（iv）图像分析和机器学习系统的硬件架构。全球缺乏专科医生，诊断往往被延误。拟议的研究将导致智能系统的开发，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系统的成功开发预计将产生可专利的知识产权，并增加加拿大经济。