Spring 2020: CS 3710 / ISSP 3565 - Advanced Topics in Artificial Intelligence

6516 Sennott Square
Monday, Wednesday
4:30PM - 5:45PM
Office hour: Thursday 1:30PM - 2:30PM, or by appointment

Seong Jae Hwang
sjh95 (at) pitt.edu
5427 Sennott Square

Alternative course title: Recent methods in medical imaging analysis:
Summary: This course will cover introductory to advanced topics in medical imaging analysis using methods in statistics, machine learning, and computer vision. Throughout the course, students will implement various methods on real medical imaging data (e.g., MRI, fMRI), see how various problems can be addressed (e.g., finding brain regions associated with disease risk factors), and understand what it means to solve them. By the end of the course, students will be able to identify problems medical imaging analysis, apply appropriate methods from various fields, and derive scientific findings from their analyses. Recommended: Basic knowledge in Linear algebra, Algorithms, and Artificial Intelligence; Basic proficiency in Matlab and Python or Java. Pre-requisites: Artificial intelligence (CS-2710), or by consent of the instructor.

  1. 1/6/20 (M): Course overview
    • Course logistics
    • What to expect
    • Research problems
  2. 1/8/20 (W): Basics in medical imaging analysis 1
    • Imaging modalities: MRI, fMRI, PET, etc.
    • Basics of image
  3. 1/13/20 (M): Basics in medical imaging analysis 2
  4. 1/15/20 (W): No Lecture
  5. 1/20/20 (M): No Lecture (MLK)
  6. 1/22/20 (W): Basics in medical imaging analysis 2
  7. 1/27/20 (M): Statistical analysis 1
    • Regression, Correlation
  8. 1/29/20 (W): Statistical analysis 2
  9. 2/3/20 (M): Spectral methods 1
  10. 2/5/20 (W): Spectral methods 2
  11. 2/10/20 (M): Computer vision in medical imaging 1
  12. 2/12/20 (W): Computer vision in medical imaging 2
  13. 2/17/20 (M): Computer vision in medical imaging 3
  14. 2/19/20 (W): Intro to machine learning and deep learning
    • Basics
  15. 2/24/20 (M): Intro to deep learning in medical imaging (a.k.a. potential project topics)
  16. 2/26/20 (W): Deep learning in medical imaging 1
  17. 3/2/20 (M): Deep learning in medical imaging 2
  18. 3/4/20 (W): Deep learning in medical imaging 3
  19. 3/9/20 (M): No Lecture (Spring Break)
  20. 3/11/20 (W): No Lecture (Spring Break)
  21. 3/16/20 (M): Short project presentation
    • Based on the initial project proposal
    • 5 min presentation + 3 min discussion
  22. 3/18/20 (W): Deep learning in medical imaging 4
  23. 3/23/20 (M): Deep learning in medical imaging 5
  24. 3/25/20 (W): Deep learning in medical imaging 6
  25. 3/30/20 (M): Topics
  26. 4/1/20 (W): Topics
  27. 4/6/20 (M): One-on-one project meeting
    • We will schedule a 5~10 min one-on-one meeting for each project during class.
  28. 4/8/20 (W): Topics
  29. 4/13/20 (M): Final project presentation 1
    • ~20 min (15 min presentation + 5 min discussion)
    • The project itself does not need to be complete by this point.
  30. 4/15/20 (W): Final project presentation 2
  31. 4/20/20 (M): Final project office hour 1 (Optional attendence)
    • I will be in the class if you want to get feedback or comment on the project.
  32. 4/22/20 (W): Final project office hour 2 (Optional attendence)
Potential paper submissions
If the project meets the submission quality, these are some of the potential venues to submit:
  1. Late April of 2020: BMVC (this is for 2019)
  2. March/April of 2020 (estimated): CVPR 2020 Workshop
  3. May 18, 2020 (estimated): NeurIPS
  4. June 30, 2020: MICCAI 2020 Workshop
  5. August 31, 2020 (estimated): AAAI
  6. September of 2020 (estimated): ICRA (this is for 2019)

Other resources
  1. Datasets: These are public datasets. Some may need to be requested which is a fairly standard and smooth procedure.
  2. Tools
    • Clinica
    • FSL: a comprehensive library of analysis tools for FMRI, MRI and DTI brain imaging data
    • SPM: a software package designed for the analysis of brain imaging data sequences

Academic integrity
All assignment submissions must be the sole work of each individual student. Students may not read or copy another student's solutions or share their own solutions with other students. Students may not review solutions from students who have taken the course in previous years. Submissions that are substantively similar will be considered cheating by all students involved, and as such, students must be mindful not to post their code publicly. The use of books and online resources is allowed, but must be credited in submissions, and material may not be copied verbatim. Any use of electronics or other resources during an examination will be considered cheating.

If you have any doubts about whether a particular action may be construed as cheating, ask the instructor for clarification before you do it. The instructor will make the final determination of what is considered cheating.

Cheating in this course will result in a grade of F for the course and may be subject to further disciplinary action.

Using an open-source codebase is accepted, but you must explicitly cite the source, especially following the owner's guideline if it exists. For any writing involved in the project, plagiarism is strictly prohibited. If you are unclear whether your work will be considered as plagiarism, ask the instructor before submitting or presenting the work.