Fall 2024 Teaching

Special Topics in Vision and Graphics:
CS 684 – Advanced Computational Methods for Biomedical Imaging



Time: MW 9:00 am – 10:15 am
Location: Register course to know

Course Instructor: Dr. Abdullah-Al-Zubaer Imran
Office: 319 Marksbury
Office Hours: Mondays 10:30 am – 12:30 pm, and by appointment.

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 be able to:

  • 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
  • The development of reliable software to implement solutions to previously mentioned topics
  • Deliver a medical imaging project
  • Gain experience in reading technical papers, and presenting research outcomes in a professional setting

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
  • Handbook of Medical Image Computing and Computer Assisted Intervention by Kevin Zhou, Daniel Rueckert, Gabor Fichtinger

Course Schedule/Outline:

WeekTopicSuggested ReadingNote
1.Aug 26:
Aug 28:
2.Sep 2:
Sep 4:
3.Sep 9:
Sep 11:
4.Sep 16:
Sep 18:
5.Sep 23:
Sep 25:
6.Sep 30:
Oct 2:
7.Oct 7:
Oct 9:
8.Oct 14:
Oct 16:
9.Oct 21:
Oct 23:
10.Oct 28:
Oct 30:
11.Nov 4:
Nov 6:
12.Nov 11:
Nov 13:
13.Nov 18:
Nov 20:
14.Nov 25:
Nov 27:
15.Dec 2:
Dec 4:
16.Dec 9:
Dec 11:
17.Dec 16: Final  

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 reviewing assignments
  • Project (40%) – proposal submission, midterm presentation, abstract submission, 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)

For undergraduate students:

  • 90–100% (A)
  • 80–89% (B)
  • 70–79% (C)
  • 60–69% (D)
  • 0–59% (E)


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 miss. So, you will get 0 out of 80 points available in Attendance & Quizzes if you miss 10 lectures in total.

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 50% score penalty if you submit within 1 day after the deadline. A score of 0 will be given for any submissions after that. Late submissions will be accepted only for certain circumstances (e.g., medical) with proper evidence.
Exceptions to this rule may be requested by providing appropriate documentation (e.g., medical) which will be considered at the discretion of the instructor.

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, DEI, Resources Available to Students

Useful Resources: