Objective

Medical imaging has been an integral part in modern healthcare procedures. Advances in deep learning have revolutionized the analysis of biomedical data, clinical diagnosis, and prognosis. In this course, students will learn fundamental image processing techniques, characteristics of different types of medical images, and how to apply different classical image processing techniques to different types of medical images. Topics covered in this course include but are not limited to:

- An overview of medical imaging modalities and their clinical use,
- Introduction to medical image computing, including registration, segmentation, classification, reconstruction, super-resolution, and visualization,
- Traditional image processing techniques for medical image analysis,
- Machine learning/deep learning for medical image analysis, and
- Frontline of AI in medical imaging and case studies.

醫學成像已成為現代醫療保健中不可或缺的一部分。深度學習的進步改變了生物醫學資料分析、臨床診斷和預後。在本課程中，學生將學習基本的影像處理技術，不同類型醫學圖像的特點，以及如何將不同的經典影像處理技術應用於不同類型的醫學圖像。本課程涵蓋的主題包括但不限於:

-醫學成像模式及其臨床應用概述，
-醫學圖像計算介紹，包括配准，分割，分類，重建，超解析度和視覺化，
-醫學圖像分析的傳統影像處理技術，
-醫學圖像分析的機器學習/深度學習，以及
-人工智能在醫學成像和案例研究中的前沿。

Syllabus

Introduction to the course and requirements. An overview of medical imaging modalities, e.g., MRI, CT, ultrasound, PET/SPECT, histopathology
An overview of medical imaging modalities and their clinical use
Introduction to medical image registration
-Clinical applications of image registration
-Linear transforms: rigid, affine
-Non-linear transforms: thin-plate spline, B-spline, diffeomorphic
-Challenges in image registrationMedical image segmentation in traditional techniques and deep learning techniques

Challenges in medical image segmentation
Semi-automated image segmentation
Medical image classification in traditional techniques
Medical image classification in deep learning techniques
Medical image reconstruction in traditional techniques and deep learning techniques
Medical image super-resolution in traditional techniques
Medical image super-resolution in deep learning techniques
Frontline of AI in medical imaging and case studies

Learning Outcome

Upon successful completion of this course, students will be able to:
1. have some basic ideas of artificial intelligence and machine learning;
2. know the medical applications of artificial intelligence and machine learning;
3. understand the limitations and possibilities of different approaches to artificial intelligence in medical image analysis;

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Objective
A series of lectures on current research in signal processing.

Syllabus

Learning Outcome

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Objective
A series of lectures on current research in solid state technology.

Syllabus

Learning Outcome

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Objective
A series of lectures on current research in wireless communication technology.

Syllabus

Learning Outcome

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