Deep Learning in Medicine
BMSC-GA 4493, BMIN-GA 3007
The use of deep networks has revolutionized areas of image recognition, speech recognition, and natural language processing. Deep networks are also transforming the world of medicine by helping doctors to improve detection, diagnosis, treatment, and management of disease. Moreover, researchers begin to incorporate deep learning methods in areas such as drug development, diagnostic radiology and personalized medicine. In this course, we will focus on the deep learning approaches that are practical and currently used in various medical disciplines. In the labs, we will implement the approaches learned in the course using available multi-modal medical dataset.
Students will learn the most common deep learning methods emerging in medicine. Students will be able to differentiate various deep learning methods and choose the most appropriate ones for specific research problems.
Tuesdays 3:00pm-5:00pm and Thursdays 4:00pm-5:00pm
Location: The Science Building, 435 East 30th St, Ground Floor, Room G19 (435 East 30th St, New York, NY, 10016)
Cem M. Deniz (firstname.lastname@example.org)
Narges Razavian (email@example.com)
Juiting Hsu (firstname.lastname@example.org)
Jingyi Su (email@example.com)
Lee Tanenbaum (firstname.lastname@example.org)
TA Office Hours (will be held before or after the Lab sections on Thursdays)
We will be using Piazza for class discussion. The system is highly catered to getting you help fast and efficiently from classmates, the TA, and us. Rather than emailing questions to the teaching staff, I encourage you to post your questions on Piazza.
Find our class page at: https://piazza.com/nyumc.org/spring2018/bmscga4493
Python 3 is preferred, Jupyter Notebook, PyTorch
Introduction to Programming (BMSC-GA 1358) or equivalent
Python Programming Language
Homeworks (40%) + Final Project (50%: 10% for proposal and final presentation, 30% for the paper) + Attendance (10%)
It is possible to audit the course for the NYU community. However, due to current classroom size restrictions, we can first allow registered students to attend the course. We are trying to find a larger classroom, if this happens, audits will be able attend the course without such a restriction.
Reading / Watching List (WiP)
Course Overview (CD, NR)
Deep Learning Basics (NR)
Deep Feedforward Networks (NR) (Lecture 3)
Convolutional Networks I (CD) (Lecture 4)
Lab 3: Deep Networks (LT)
Student Project Proposal Presentations (one page papers are due at 3:00 pm)
Feedback to Student Project Proposals
Convolutional Networks II (CD)
Lab 4: CNNs (LT)
Optimization of Deep Networks (CD) (Lecture 6)
Guest Lecture (Krzysztof J. Geras)
No Class (Spring Break)
Lab: Regularization (JS)
Practical Methodology and Applications (CD) (Lecture 8)
Recurrent and Recursive Nets (NR) (Lecture 9)
Lab: RNNs (JH)
Generative Adversarial Networks (NR)
Lab: GANs (JS)
Autoencoders (CD) (Lecture 11)
Lab: Autoencoders (JH)
Transfer Learning (CD, NR) (Lecture 12)
No Class (Finalize your projects)
No Lab ( Finalize your projects)
Student Project Paper Deadline (3:00 pm)
Student Project Presentations
There will be four homeworks each will be graded for 10% of your final grade.
HW1 (due 02/13 3:00 pm): Regression related to medical field - getting familiar with deep learning tools
HW2 (due 03/16 5:00 pm): Basics of convnets - finding abnormalities on a medical dataset
HW3 (due 04/10/2018 3:00 pm): Sequence Classification¶
HW4 (due 05/12/2018 5:00 pm): Transfer learning
Students are expected to work on a project related to the topics presented in the lectures.
You may work in teams of up to four on the final project. Teams must be finalized by the project proposal deadline (02/20).
Students are free to choose their project topics related to medicine. Example research topics are:
NYU internal (if you are interested in one of these topics and learn more about them, please send an email to email@example.com so I can share the contact information with your team)
You must submit a your one page proposal and make a presentation to receive useful feedback on the project.
The final report should include the description of the task, models, experiments and conclusion. It can be up to 6 pages long excluding unlimited pages reserved for references. In the final week of the semester you will present your findings in the class. Both of your paper and presentation will affect your grade on the student project.
The final report must state the contributions of each team member. Team projects which fail to include this will receive a 2.5% grade deduction.
You must adhere to the due dates for all required submissions. If you miss a deadline, then you will not get credit for that assignment/post.
No "Incomplete" will be assigned for this course unless we are at the very end of the course and you have an emergency.
We use piazza for all course related questions/discussions related to the course. If you have highly private/sensitive issues and prefer to communicate via email, please keep in mind that: we will check emails daily during the week, and we will respond to you within 48 hours.
We will make announcements throughout the semester by e-mail.
Make sure that your email address is updated; otherwise you may miss important emails from me.
Always backup your work on a safe place (electronic file with a backup is recommended) and make a hard copy. Do not wait for the last minute to do your work. Allow time for deadlines.
Plagiarism, the presentation of someone else's words or ideas as your own, is a serious offense and will not be tolerated in this class. The first time you plagiarize someone else's work, you will receive a zero for that assignment. The second time you plagiarize, you will fail the course with a notation of academic dishonesty on your official record.