SDST4023 Medical Image Analysis (6 credits) Academic Year 2025
Offering Department SCDS (Department of Statistics and Actuarial Science) Quota 15
Course Co-ordinator TBC, SCDS (Department of Statistics and Actuarial Science) < ugenq@hku.hk >
Teachers Involved
Course Objectives Medical imaging has been a critical part in modern healthcare procedures. Its primary use is to visualize the human body at different levels (e.g., at organ, tissue, cell, and molecular levels) using different imaging modalities (e.g., those in radiology, pathology, dermatology, ophthalmology, microscopy, and genetics). The objective of this course is to provide students with an overview of the machine learning and deep learning methods in medical image processing and analytics. We will study many of the current methods used to enhance and extract useful information from medical images. A variety of medical image diagnostic scenarios will be used as examples to motivate the methods.
Course Contents & Topics This course covers the basic concepts and computational methods (especially machine learning and deep learning methods) in medical image analysis. Topics covered in this course include but are not limited to:
- An overview of medical imaging modalities,
- An overview of medical image analysis applications and their challenges,
- Traditional image processing techniques for medical image analysis,
- Basics of machine learning/deep learning techniques,
- Machine learning/deep learning for medical image analysis, and
- Case studies.
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 understand the basic concepts and motivation of medical image analysis
CLO 2 learn about the various applications and challenges of medical image analysis
CLO 3 learn about the computational techniques behind modern medical image analysis
CLO 4 gain hands-on experience on building practical computational models for medical image analysis
CLO 5 get expose to current research topics in medical imaging
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Pass or already enrolled in SDST3600, and Pass in (COMP2113 or COMP2119 or COMP2396); and
Not for students who have passed in APAI4023, or already enrolled in this course.
Recommended: familiarity with machine learning/deep learning; strong programming skills (Python/PyTorch will be used in this course)
Only for students admitted in 2025 and thereafter.
Course Status with Related Major/Minor /Professional Core 2U000C00 Course not offered under any Major/Minor/Professional core
2026 Professional Core in Decision Analytics ( Disciplinary Elective )
2026 Major in Decision Analytics ( Disciplinary Elective )
2025 Professional Core in Decision Analytics ( Disciplinary Elective )
2025 Major in Decision Analytics ( Disciplinary Elective )
Course to PLO Mapping 2026 Professional Core in Decision Analytics < PLO 1,2,3,4 >
2026 Major in Decision Analytics < PLO 1,2,3,4 >
2025 Professional Core in Decision Analytics < PLO 1,2,3,4 >
2025 Major in Decision Analytics < PLO 1,2,3,4 >
Offer in 2026 - 2027 N        Examination
Offer in 2027 - 2028 N
Course Grade A+ to F
Grade Descriptors
A Demonstrate thorough mastery at an advanced level of extensive knowledge and skills required for attaining all the course learning outcomes. Show strong analytical and critical abilities and logical thinking, with evidence of original thought, and ability to apply knowledge to a wide range of complex, familiar and unfamiliar situations. Apply highly effective organizational and presentational skills.
B Demonstrate substantial command of a broad range of knowledge and skills required for attaining at least most of the course learning outcomes. Show evidence of analytical and critical abilities and logical thinking, and ability to apply knowledge to familiar and some unfamiliar situations. Apply effective organizational and presentational skills.
C Demonstrate general but incomplete command of knowledge and skills required for attaining most of the course learning outcomes. Show evidence of some analytical and critical abilities and logical thinking, and ability to apply knowledge to most familiar situations. Apply moderately effective organizational and presentational skills.
D Demonstrate partial but limited command of knowledge and skills required for attaining some of the course learning outcomes. Show evidence of some coherent and logical thinking, but with limited analytical and critical abilities. Show limited ability to apply knowledge to solve problems. Apply limited or barely effective organizational and presentational skills.
Fail Demonstrate little or no evidence of command of knowledge and skills required for attaining the course learning outcomes. Lack of analytical and critical abilities, logical and coherent thinking. Show very little or no ability to apply knowledge to solve problems. Organization and presentational skills are minimally effective or ineffective.
Communication-intensive Course N
Course Type Lecture-based course
Course Teaching
& Learning Activities
Activities Details No. of Hours
Lectures 36.0
Tutorials 12.0
Reading / Self study 100.0
Assessment Methods
and Weighting
Methods Details Weighting in final
course grade (%)		 Assessment Methods
to CLO Mapping
Assignments Coursework (assignments, tutorials, and class test(s); may include term project) 50.0 1,2,3,4,5
Examination One 2-hour written examination 50.0 1,2,3,5
Required/recommended reading
and online materials
Course Website http://moodle.hku.hk
Additional Course Information