Image Analysis and Machine Learning Techniques for Computer-aided Diagnosis

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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.