* Important Notice. We will have both office hour and class through ZOOM (Same Time as Scheduled)
Classroom: https://vanderbilt.zoom.us/j/613153831
TA Office hour: https://vanderbilt.zoom.us/s/779998946
Faculty Office hour: https://vanderbilt.zoom.us/j/613153831
I. Course Information：
Deep Learning is prevalent since 2015 in medical image computing. This class covers the theories and practices of Deep Learning related to medical image computing.
II. Teaching Team:
Instructor: Yuankai Huo Email: yuankai.huo@vanderbilt.edu
Teaching Assistant: Roza Bayrak Email: roza.g.bayrak@vanderbilt.edu
Class Meets: Tuesday & Thursday, 4:00 pm – 5:15 pm, FGH 110
TA Office Hours: Tuesday & Thursday, 1:00 pm – 4:00 pm, TA Office (FGH 385 or 375)
Instructor Office Hours: Tuesday & Thursday, 3:00 pm – 4:00 pm, (FGH 376)
Course Website: https://my.vanderbilt.edu/cs8395
Submission & Discussion: https://www.vanderbilt.edu/brightspace
III. Schedule
|
Date |
Topics |
Comments |
|---|---|---|
| Jan 07 | Overview of Deep Learning in Medical Image Computing | slides |
| Jan 09 | Neural Networks and CNN | slides, reading1 |
| Jan 14 | Classification (Medical Image Diagnosis) | slides, reading2 |
| Jan 16 | Detection (Landmark Localization and Detection) | slides, reading3 |
| Jan 21 | Segmentation (Medical Image Segmentation) | slides, reading4 |
| Jan 23 | GAN (Medical Image Synthesis) | slides, reading5 |
| Jan 28 | Assignment 1 Presentation | |
| Jan 30 | Multi-modal Learning | slides, reading6 |
| Feb 04 | Multi-task Learning | slides, reading7 |
| Feb 06 | Beyond 2D: 3D Networks | slides, reading8 |
| Feb 11 | Semi-/weakly-supervised Learning | slides, reading9 |
| Feb 13 | Assignment 2 Presentation | |
| Feb 18 | Unsupervised Learning | slides, reading10 |
| Feb 20 | Data Augmentation and Preprocessing | slides, reading11 |
| Feb 25 | Final Project Proposal | example |
| Feb 27 | Final Project Proposal | |
| Mar 03 | No Class: Spring Break! | |
| Mar 05 | No Class: Spring Break! | |
| Mar 10 | No Class: COVID-19 | |
| Mar 12 | No Class: COVID-19 | |
| Mar 17 | Attention Mechanism and Postprocessing | slides, reading12 |
| Mar 19 | Spatial-temporal Model: RNN and LSTM | slides, reading13 |
| Mar 24 | Assignment 3 Presentation | |
| Mar 26 | Image Retrieval and Active Learning | slides, reading14 |
| Mar 31 | Image Text Co-learning | slides, reading15 |
| Apr 02 | Learn from Prior and Online Learning | slides, reading16 |
| Apr 07 | Summary | |
| Apr 09 | Final Project Presentation | |
| Apr 14 | Final Project Presentation | |
| Apr 16 | Final Project Presentation |
IV. Assignments
|
Assignments |
Download |
Due Date |
|---|---|---|
| Reading Assignments | Template | Begining of the class |
| Assignment 0: Eligibility Test | Jan 09 2020, 4:00 pm | |
| Assignment 1: Detection | Jan 28 2020, 9:00 am | |
| Assignment 2: Classification | Feb 13 2020, 9:00 am | |
| Assignment 3: Segmentation | Mar 26 2020, 9:00 am | |
| Final Project | Example | Apr 20 2020, 9:00 am |
* Teamwork is not allowed for assignments.
V. Assignments, Mid Term Exam, and Final Project
More details are provided Here.
VI. Computational Resource
GPU computing is required for this class. I strongly recommend to use your own/lab’s GPU since that is the most convenient way of writing and testing code with GUI. However, if you don’t have any GPU computational resource, you can use the GPU on ACCRE by filling the following two forms:
1. ACCRE registration form
2. GPU requirement form
If you are already using ACCRE, you can submit a helpdesk ticket instead to request access to your class group. There is a relatively large demand for GPU resources at ACCRE, so limited GPU resources will be provided for this class.
*The deadline of ACCRE resource application is the due date of assignment 1.
VII. FAQs
1. The class is full. Can I still get in?
It is unlikely except other students drop it during the first week.
2. What is pre-requirement?
Linear algebra, programming in python, introduction in machine learning.
3. Can I sit in class without registering?
Yes after getting the instructor’s approval. Another option is to register to audit the class (just $50).
VIII. References
* We used images and contents in the slides from the following resources, thanks for the great work done by the smart people!
http://deeplearning.cs.cmu.edu/
https://www.cs.princeton.edu/courses/archive/spring16/cos495/
http://ttic.uchicago.edu/~shubhendu/Pages/CMSC35246.html
https://www.cc.gatech.edu/classes/AY2018/cs7643_fall
https://www.deeplearningbook.org/lecture_slides.html
http://introtodeeplearning.com/