CS 684 – Advanced Computational Methods for Biomedical Imaging

https://aaz-imran.github.io/teaching/2026-fall-cs684

Syllabus

Time: MWF 9:00 AM – 9:50 AM

Course Instructor: Dr. Abdullah-Al-Zubaer Imran
Office: 319 Marksbury
Office Hours: Wednesdays 10 AM - 12 PM

Course Description:
The field of imaging science is undergoing rapid growth. Biomedical imaging and its analysis play a fundamental role in comprehending, visualizing, and quantifying medical images for various clinical applications. Computational image analysis techniques can significantly improve disease diagnosis by making it faster, more effective, and fairer. This seminar course will focus on understanding different imaging tasks associated with medicine and applications of advanced computational methods (such as computer vision and deep learning) to solve important biomedical imaging problems. The course will also examine some key topics and advanced techniques in computer vision and medical imaging, reading, reviewing, presenting, and discussing papers published in major computer vision and medical imaging venues (e.g., CVPR, IEEE TMI, MedIA, MICCAI, ICCV, etc.). The course will be taught with a good mix of theory and applications, with different case studies.

Course Description:
The field of imaging science is undergoing rapid growth. Biomedical imaging and its analysis play a fundamental role in comprehending, visualizing, and quantifying medical images for various clinical applications. Computational image analysis techniques can significantly improve disease diagnosis by making it faster, more effective, and fairer. This seminar course will focus on understanding different imaging tasks associated with medicine and applications of advanced computational methods (such as computer vision and deep learning) to solve important biomedical imaging problems. The course will also examine some key topics and advanced techniques in computer vision and medical imaging, reading, reviewing, presenting, and discussing papers published in major computer vision and medical imaging venues (e.g., CVPR, IEEE TMI, MedIA, MICCAI, ICCV, etc.). The course will be taught with a good mix of theories and applications with different case studies.

Topics include:

• Introduction to digital image processing and computer vision
• Major imaging modalities (e.g., X-ray, CT, MRI, Ultrasound)
• Fundamental image processing techniques
• Machine learning and its applications to biomedical imaging
• Deep learning and its applications to biomedical imaging
• Important image databases, ML/DL, and Medical Imaging tools
• Challenges in medical imaging and recent computational methods to tackle them
• Reading and writing medical imaging research papers

Course Outcomes:
Upon completion of this course, students will:

• Understand different medical imaging systems and their usages
• Comprehend basic and advanced image processing techniques
• Analyze imaging data related to problems in medicine
• Use ML/DL and medical imaging tools
• Identify and solve medical image analysis tasks by applying appropriate computational methods
• Define and implement their own project that explores an important problem in medical imaging
• Gain experience in reading technical papers and presenting research outcomes in a professional setting

Prereqs:
Course prerequisite: Programming ability at an intermediate level, familiarity with probability and statistics, knowledge of Python (highly recommended), machine learning, or approval of the instructor.

Required books:
There is no required textbook for CS 684.

Recommended books:

• Digital Image Processing 4th Ed. by Gonzalez and Woods
• Insight into images: principles and practice for segmentation, registration, and image analysis by Terry S. Yoo
• Deep Network Design for Medical Image Computing by Haofu Liao; Kevin Zhou; Jiebo Luo
• Fundamentals of Medical Imaging 2nd Ed. by Paul Suetens

Course Schedule/Outline (Tentative):

Course Activities:

• Class participation (15%) – lecture quizzes, class performance, and debate
• Paper discussion (15%) – paper presentations and online discussions
• Assignments (30%) – two coding and a paper review assignment
• Project (40%) – proposal submission, midterm presentation, final presentation, and code and report submission.

Grading Scale:
After the final percentage grade is calculated, the following scale will be used to determine the final letter grade.

For graduate students:

• 90–100% (A)
• 80–89% (B)
• 70–79% (C)
• 0–69% (E)

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Attendance Policy: Students are required to attend every lecture. Students only present in the class can take the associated quiz and get the available points. Missing a maximum of 2 lectures will be excused. Any student missing more than 2 lectures without any reasonable excuses will start losing 10 points for every absence.

Academic Integrity: Please strictly follow the Academic Offenses Rules (plagiarism, cheating, and falsification or misuse of academic records). Also, keep in mind that any copyrighted materials (e.g., images and other media), and published contents (e.g., academic papers, books, web sources, online tools) used in your submissions and project should be properly cited. Ideas from people other than your own (for the project—ideas from outside your group) should be acknowledged.

Late Policy: Late submissions (assignment, project proposal, code, project final report) will be subject to a 1% score penalty per hour post-deadline. A score of 0 will be automatically assigned for any submissions made four days after the deadline. Late submissions will be accepted only for certain circumstances (e.g., medical emergency) with proper evidence.
Exceptions to this rule may be requested by providing appropriate documentation which will be considered at the discretion of the instructor.

Generative AI Policy: GenAI tools such as ChatGPT may be used in this course for the purposes of pre-submission activities. However, students are responsible for submitting work that meets the assignment standards for quality and factual accuracy. Check the Canvas page for more detailed instructions on this. If you have any questions or concerns about the policy, contact the instructor before submitting any assignments.

Disability and Special Accommodation: Please let the instructor know of any needed accommodation in the first two weeks. Please also see Academic Accommodation for further assistance.

Academic Policy Statements, Resources Available to Students

Useful Resources:

• UK Libraries
• Free tutoring and coaching at UK
• Google Scholar
• Image Datasets
• PyImageSearch