CS5540: Computational Techniques for Analyzing Clinical Data
Course description and pre-requisites
CS5540 is a masters-level course that covers a wide range of clinical
problems and their associated computational challenges. The practice of
medicine is filled with digitally accessible information about patients,
ranging from EKG readings to MRI images to electronic health records. This
poses a huge opportunity for computer tools that make sense out of this
data. Computation tools can be used to answer seemingly straightforward
questions about a single patient's test results (“Does this patient
have a normal heart rhythm?”), or to address vital questions about
large populations (“Is there any clinical condition that affects the
risks of Alzheimer”). In CS5540 we will look at many of the most
important sources of clinical data and discuss the basic computational
techniques used for their analysis, ranging in sophistication from current
clinical practice to state-of-the-art research projects.
There are no pre-requisites beyond programming skill at the level of
CS2110, although some familiarity with elementary statistics and
algorithms would be helpful. The course is being taught in conjunction
with Ashish Raj
from Cornell's Weill Medical College, and several lectures will be given
Credit: 3 credits, grade or S/U.
Meeting: WF 1:25–2:40 in 315
Course staff and office hours
Zabih (4130 Upson Hall; office hours TBA) and Ashish Raj (Weill
Cornell Medical College).
TA: Devin Kennedy (office
hours: F 2:30–5:00 PM in UP 317, or by appointment).
- Introduction to medical data and signals: examples drawn from
hospital records, EEG, CT, MRI.
- Methods for processing of 1D medical signals
- Arrythmia and heart murmur detection from EEG signals
- Statistically optimal estimation and detection of 1D signals
- Methods for processing multi-dimensional medical signals
- Statistically optimal estimation and detection
- MRI image reconstruction
- Image segmentation
- Medical applications of machine learning
- Classification, clustering and segmentation of tumor/non-tumor
voxels from contrast-enhanced MRI of liver and brain
- Classification of patients' disease state from imaging and
Grading and course policies
Details are still being finalized, but the course will include two or
three programming assignments, to be completed in groups of two. There
will also be a final project.
There will be a few short, in-class quizzes; the purpose of these
quizzes is primarily to ensure that students are keeping up with lectures,
so they should be fairly easy.
Announcements will be posted here as they come up. You can also follow
the course RSS feed.
- (7 March) Assignment 1 has been updated and the due date has been
changed to 15 March.
- (13 March) Assignment 1 has been updated again; we've added a new
commandline argument to the contract which you may elect to
support. Additionally, we've provided some extras which you may find
- (31 March) You can consult the project
list if you are looking for ideas for your final project.
- (27 January) Introduction — AED, drug side
effects, epilepsy diagnosis; includes “Computational Techniques
Applicable to Medical Data: One Clinician's View” (lecture given
by Gary S. Dorfman, M.D.). PDF, PPTX. References and further reading.
- (29 January) Introduction — Detection,
Estimation, Classification. PDF, PPTX. References and further reading.
- (3 February) General Methods for Analyzing 1D Data
— zero-crossings, local averaging, convolution, matched filters.
PDF, PPTX. References and further reading.
- (5 February) Linear Time Invariance —
convolution, random variables, limit theorems, linearity, edge
detection. PDF, PPTX. References and further reading.
- (10 February) Lecture 5: Transforms — Fourier
and Wavelets. PDF, PPTX. References and further reading.
- (12 February) Lecture 6: Classification —
k-NN, validation, statistical classification. PDF, PPTX. References and further reading.
- (17 February) Lecture 7: Estimation — Least
Squares, Maximum Likelihood. PDF, PPTX. References and further
- (19 February) Lecture 8: ECG Analysis (Guest
Lecturer: Ken Birman). PDF.
- (24 February) Lecture 9: Estimation continued
— Maximum Likelihood and Maximum a Posteriori Estimation. PDF, PPTX. References and further
- (26 February) Class canceled due to
- (3 March) Estimation Continued — Maximum a
Posteriori Estimation. PDF, PPTX. References and further
- (5 March) Decisions Based on Densities —
Expectation Maximization. PDF, PPTX. References and further reading.
- (10 March) (pending)
- (12 March) (pending)
- (17 March) Graph cuts. PDF, PPTX.
- (19 March) (pending)
- (31 March) Graph cuts. PDF, PPTX.
- (2 April) (pending)
- (7 April) Graph cuts. PDF, PPTX.
- (9 April) (pending)
- (14 April) PDF, PPTX.
- (16 April) Accelerated MRI reconstruction. PDF, PPTX.
- (21 April) Automated dose tracking (guest lecture by
George Shih and Devin Kennedy). (slides pending)
- (23 April) Dynamic MRI image reconstruction. PDF, PPTX.
Lecture notes and slides will be posted here as they become
Assignments will be posted here as they become available. Submissions
and grading will be managed through CMS.
- Project 1: Analyzing and classifying ECGs. Released 17 February
(updated on 7 March); due
15 March. Specification, Data (warning: 94MB download). Useful
extras: a MATLAB
function for plotting annotated signals; sampling frequencies for normal and shockable signals
in the provided data.
- Project 2: Binary segmentation. Released 21 April; due 7
May. Specification, Data, patched GC library.