Miaomiao Zhang
CSE / BIOE 420 / 320: Biomedical Image Computing and Modeling
Description: This course focuses on an in-depth study of advanced topics and interests in image data analysis. Students will learn about hardcore imaging techniques and gain mathematical fundamentals needed to build their own models for an effective problem solving. Topics of deformable image registration, numerical analysis, probabilistic modeling, data dimensionality reduction, and convolutional neural networks for image segmentation will be covered. The main focus might change from semester to semester. Credit will not be given for both CSE 320 and CSE 420. Prerequisite: (Math 205 or Math 43) and CSE 017, or consent of instructor.
Class meetings: TR 1:10pm-2:25pm @ Whitaker Lab 270
Instructor: Miaomiao Zhang (miaomiao -at- cse.lehigh.edu)
Office hours: TR 3:30pm-4:30pm @ PL514A, or by appointment
Lecture materials including slides and notes will be posted on Course Site
Grading
Projects (4 course projects and 1 final project) (70%)
Presentation (20%)
Participation (10%)
No exams are required in this course. All programming will be in Matlab, a powerful, free programming tool for Lehigh students: https://software.lehigh.edu/install/. All reports must be written in LaTeX and submitted as a PDF.
Homeworks are due by midnight (11:59:59 PM) on the due date. Late assignments will not be accepted.
Schedule
| Date | Topics | Projects |
| Week 1 | Aug 29 | Course Introduction |
| Aug 31 | Introduction to Image Analysis and Basic Variational Methods | |
| Week 2 | ||
| Sep 5 | Image Denoising I | |
| Sept 7 | Image Denoising II: Total Variation | PS1, due on Sep 26 |
| Week 3 | ||
| Sep 12 | Basics of Deformable Image Registration | |
| Sep 14 | Diffeomorphisms | |
| Week 4 | ||
| Sep 19 | TBD | |
| Sep 21 | Geodesic Shooting I | |
| Week 5 | ||
| Sep 26 | Geodesic Shooting II | PS1 due |
| Sep 28 | Diffeomorphic Image Registration: Adjoint Methods | PS2, due on Oct 24 |
| Week 6 | ||
| Oct 3 | Fréchet mean, atlas estimation | Oct 5 | Data Dimensionality Reduction: Principal Component Analysis (PCA) |
| Week 7 | ||
| Oct 10 | Guest lecture, Dr. Chao Zhou @ECE | |
| Oct 12 | TBD | |
| Week 8 | ||
| Oct 17 | Pacing break | |
| Oct 19 | Nonlinear PCA methods, kernel trick | |
| Week 9 | ||
| Oct 24 | Bayesian Methods | PS2 due |
| Oct 26 | Maximum a Posteriori (MAP), maximum likelihood (MLE) | PS3, due on Nov 14 |
| Week 10 | ||
| Oct 31 | Image Clustering: Gaussian Mixture Model | |
| Nov 2 | Expectation Maximization I | |
| Week 11 | ||
| Nov 7 | Expectation Maximization II | |
| Nov 9 | Markov Chain Monte Carlo Methods | |
| Week 12 | ||
| Nov 14 | Metropolis–Hastings | PS3 due |
| Nov 16 | Hamiltonian Monte Carlo | PS4, due on Dec 5 |
| Week 13 | ||
| Nov 21 | Image Classification / Segmentation | Final project, due on Dec 14 |
| Nov 23 | Thanksgiving break | |
| Week 14 | ||
| Nov 28 | Deep Learning I | |
| Nov 30 | Deep Learning II | |
| Week 15 | ||
| Dec 5 | Paper presentation | PS4 due | Dec 7 | Paper presentation |
| Dec 14 | Final project Report Due |
Disclaimer
The instructor reserves the right to make changes to the course schedule, syllabus, and project deadlines. Changes will be announced early in advance.