Spring 2023 Teaching

CS 585/685 – Advanced Computational Methods for Biomedical Imaging



Time: MW 3:00 pm – 4:15 pm
Location: Register course to know

Course Instructor: Dr. Abdullah-Al-Zubaer Imran
Office: 319 Marksbury
Office Hours: Wednesdays 9 am – 11 am, and by appointment.

Course Description:
This project-based course focuses on understanding different imaging tasks associated with medicine and applications of advanced computational methods, such as ML/DL to solve medical imaging problems. 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 585/685.

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.Jan 9: Course Introduction, Logistics, Expectations
Jan 11: Medical Imaging Modalities, Project Ideas

Biomedical Imaging Modalities
2.Jan 16: No lecture - MLK Day
Jan 18: Image Processing: Image Transformation, Feature Extraction, Etc.

Digital Image Processing, Mathematical methods in medical image processing
release of hw1
3.Jan 23: Machine Learning, Hands-on
Jan 25: Project Lifecycle, Machine Learning for Medical Image Processing, Hands-on
Machine Learning for Medical Imagingpaper bidding due at 11:59 pm
4.Jan 30: Deep Learning
Feb 1: Deep Learning for Medical Imaging
Deep Learning by I. Goodfellow
Deep Learning in medical image analysis

hw1 due at 11:59 pm
5.Feb 6: Deep Generative Models
Feb 8: Vision Transformers
 release of hw2
project proposal due at 11:59 pm
6.Feb 13: Masked Image Modeling Advances 3D Medical Image Analysis (Presenter: A. Imran)
Feb 15: Crowd Counting in the Frequency Domain (Presenter: C. Archbold)
7.Feb 20: Vision-Language Transformer and Query Generation for Referring Segmentation (Presenter: S. Gupta)
Feb 22: FAT-Net: Feature adaptive transformers for automated skin lesion segmentation (Presenter: S. Eskandari)
hw2 due at 11:59 pm
8.Feb 27: MLP-Mixer: An all-MLP Architecture for Vision (Presenter: Y. Jiang)
Mar 1: PhysGNN: A Physics–Driven Graph Neural Network Based Model for Predicting Soft Tissue Deformation in Image–Guided Neurosurgery (Presenter: C. Ti)
9.Mar 6: Project Midterm Presentation
Mar 8: Project Midterm Presentation
debate team split
10.Mar 13: No Lecture – Spring Break
Mar 15: No Lecture – Spring Break
11.Mar 20: DASO: Distribution-Aware Semantics-Oriented Pseudo-Label for Imbalanced Semi-Supervised Learning (Presenter: L. Nadeesha)
Mar 22: Content-Variant Reference Image Quality Assessment via Knowledge Distillation (Presenter: M. Ahamed)
12.Mar 27: [Debate] AI is Too Advanced for (Imaging) Medicine
Mar 29: Towards Low-Cost and Efficient Malaria Detection (Presenter: S. Alsalman)
13.Apr 3: Project Discussion
Apr 5: Guest lecture I: Generalizable Learning in Medical Image Analysis
release of hw3
14.Apr 10: C-CAM: Causal CAM for Weakly Supervised Semantic Segmentation on Medical Image (Presenter: A. Bhattacharyya)
Apr 12: T-AutoML: Automated Machine Learning for Lesion Segmentation Using Transformers in 3D Medical Imaging (Presenter: M. Gokmen)
15.Apr 17: Diffusion Models in Medical Imaging
Apr 19: Guest lecture II – Medical Imaging in Industry
hw3 due at 11:59 pm
16.Apr 24: Prep Days – Concluding Remarks and Final Discussion
Apr 26: No lecture – Reading day
17.May 1: Final Project Presentation (Scheduled as the Final Exam) final report due at 11:59 pm

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: