SYLLABUS
MATH 4/5779 – Math Clinic
Medical Image Analysis Using Artificial Neural Nets in Support of Detection,
Diagnosis, and Prognosis
Sponsor: University of Colorado Department of Radiation Oncology
Spring Semester - 2010
Instructors: Weldon A. Lodwick and Francis Newman
Research Assistant: Shoshona Rosskamm
Office: Weldon Lodwick, UC-Denver Building, Room 643
Francis Newman, UC-Denver Building, Room 609
Shoshona Rosskamm, UC-Denver Building, Room 609
Telephone: 303.556.8462 (office), 303.556.8442 (secretary), 303.556.8550 (fax)
AMC, Francis 720.848.0134
E-Mail: UCD-DDC, Weldon Lodwick weldon.lodwick@ucdenver.edu
UCD-AMC, Francis Newman francis.newman@ucdenver.edu
UCD-DDC, Shoshana Rosskamm shoshana.rosskamm@email.ucdenver.edu
Web Site: Instructors’ website http://www-math.ucdenver.edu/~wlodwick
Clinic website http://www-math.cudenver.edu/~clinic/5779/
Office Hours: Mondays/Wednesdays 4:25pm – 5:25pm
Tuesdays 11:00am – 12:30pm
Other times by arrangement
Text: Readings in neural networks – general and specific neural networks (ACM,
Auto CM, and J-Net) to be handed out
Neural Network Design by Martin T. Hagan, Howard B. Demuth, and Mark
Beale, PWS Publishing Company, 1996 (optional – selected chapters can be made
available, the first four chapters can be downloaded from
http://hagan.ecen.ceat.okstate.edu/nnd.html ... thanks Ed Gard).
Students with Disabilities: If you have a disability that requires accommodation
in this course, please see me as soon as possible. We are happy to make
appropriate accommodations, provided timely notice is received.
Cell Phones: You are to turn off your cell phones prior to entering class.
Objective of this Mathematics Clinic: The clinic is primarily a pedagogical tool
where one learns applied mathematics by solving problems faced by the
Department of Radiation Oncology. Working in research teams to develop results
associated with a project (solving a set of problems and presenting the results) is
an integral part of every clinic. Thus, we will try to solve problems that are of
current concern. In particular, for this semester, our objectives will be:
 To develop a software system utilizing artificial neural networks,
and/or others that we may uncover over the course of the semester for
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computer assisted detection of structures of interest, diagnosis, and
prognosis.
Therefore, upon completion the student should have a good foundation in the
mathematics of artificial neural networks, and how to apply them to medical
images. The “deliverable” to our sponsor is MATLAB code that (or makes
good progress such that it) performs:
1. Computer Assisted Prognosis: Non-small cell lung cancer (NSCL) and
limited small cell lung cancer (LSCLC) patients respond, do not respond,
or remain unchanged upon receiving chemotherapy given to reduce the
size of the tumor. We will apply artificial neural networks (ANN’s),
specifically J-Net (and other architectures) to computed tomography (CT)
images from the NSCL and LSCLC patients before and after
chemotherapy to determine if responders, non-responders, and unchanged
patients can be predicted from the ANN image analysis. J-Net has shown
that it can find “harmonics” in images that are not responses to
chemotherapy and the question is, “Can ANN’s be trained to predict
responses from patients whose images are not in the training set?”
2. Computer Assisted Detection and Diagnosis: It has been shown that one
can train ANN’s to differentiate between normal versus abnormal lungs
and between different lung pathologies in CT chest images. The goal is to
train ANN’s on CT’s to determine the effectiveness of differential
diagnosis of these conditions. Can the ANN’s achieve better than 90%
success rate in the differential diagnosis? What does the confusion matrix
look like?
3. Computer Assisted Brain Morphology and IQ Analysis in Pediatric
Patients after Radiation Treatment of Brain Tumors: After cancer
treatment to the central nervous system, pediatric patients may undergo
noticeable changes in bran morphology. Multiple magnetic resonance
images (MRI’s) are taken of pediatric cancer patients after their treatment
to monitor changes. An IQ test may be administered post-treatment as
well. A semi-automatic segmentation routine has been employed on the
post-treatment MRI’s to measure brain morphology. That is, volumes of
certain brain regions such as the right cerebral cortex are measured (albeit
imperfectly because of motion artifacts common in pediatric patients).
Can one determine a correspondence between the changes in brain
morphology and IQ?
4. Computer Assisted Brain Lesion Detectors: Find all lesions and/or MS
scabs in an MRI or CT.
To accomplish these objectives, the clinic will split up into several teams to work
on a semester project. Each individual will be working on subtasks leading to the
completion of the team project and each team will have a team leader to
coordinate the tasks. Software development involves research to create, to test, to
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analyze, and to document (the software).
Prerequisites
Linear algebra, advanced calculus or junior level engineering mathematics or
mathematical physics. The student should have some familiarity with MATLAB
or be ready to learn quickly.
Assignments
There are three problem sets which will be questions and calculations associated
with the readings. These are to be done individually.
Implementation
Each our four teams will implement one of the following systems either by
writing their own MATLAB application or by using Semeion software: J-Net,
Auto CM, ACM, and probabilistic (Bayesian) ANNs.
PROPOSED COURSE OUTLINE
The proposed outline is the initial guess of the topics that will be fruitful to
investigate. Research is a process of discovery when one does not know, so the
rule is that we will modify our topics during the semester. Thus the proposed
outline will undoubtedly change as we learn more during the semester.
The tentative topics we will cover are:
I.
Introduction
A. Conduct of the course, expectations, assignments, projects
B. Problem statement for this semester’s Math Clinic
II.
Artificial Neural Networks (from Neural Network Design)
A. Example problems and architectures (Chapters 2/3)
B. Perceptron (Chapter 4)
C. Hebbian Learning (Chapter 7)
D. Backpropagation (Chapter 11)
E. Associative Learning (Chapter 13)
III.
Specialized Neural Networks (from articles handed out)
A. Bayesian Neural Networks, Probabilistic Neural Networks
B. J-Net
C. ACM
D. Auto CM
E. Adaptive Resonance Theory (Chapter 16)
IV.
Other Approaches (it will depend on what is uncovered during the
course of the semester)
Important Dates/Tentative Schedule
Week of:
Topics Covered
January 20- February 20
Introduction to the technical aspects of the clinic.
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February 12
Problem Set 1 due (5pm)
February 19
Draft Project Proposal
February 26
Written Project proposal due methods, materials,
schedule, division of labor (5pm)
March 12
Problem Set 2 due (5pm)
March 17
Progress report and annotated bibliography –
Written paper and a 15 minute oral presentation by
each team
April 9, 12, 16, 19, 23, 26
Implementations
April 16
Problem Set 3 due (5pm)
May 7
May 10 or 12
Final written reports (with software) due (5pm)
Presentations, 30 minutes/group
Projects
You are, in conjunction with us, to choose projects from the following list.
Projects could evolve, be added, subtracted or modified during the course - not
arbitrarily but as a result of circumstances.
Project 1: Develop a computer assisted prognosis system in MATLAB that
applies artificial neural networks (ANN’s), specifically J-Net (and other
architectures) to computed tomography (CT) images from the NSCL and LSCLC
patients before and after chemotherapy to determine if responders, nonresponders, and unchanged patients can be predicted from the ANN image
analysis.
Project 2: Develop a computer assisted detection and diagnosis system in
MATLAB that applies artificial neural networks (ANN’s) that one can train
ANN’s to differentiate between normal versus abnormal lungs and between
different lung pathologies in CT chest images. The goal is to train ANN’s on
CT’s to determine the effectiveness of differential diagnosis of these conditions.
Part of the project will be devoted to looking at the applicability of Bayesian
decision networks (probabilistic neural networks).
Project 3: Develop a MATLAB system to assist in brain morphology and IQ
analysis for pediatric patients who have undergone radiation treatment of brain
tumors to determine a correspondence between the changes in brain morphology
and IQ. The techniques that are best suited for this project are one of the things
that the project will investigate. Auto CM would be one that should be relevant.
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Project 4: Computer assisted detection, CADe, of brain lesions, this project will
employ ANNs (novel or traditional) to find lesions in MRIs but perhaps in CTs as
well. A substantial database of MRIs with multiple sclerosis (MS) plaques exists
and it is of diagnostic significance to be able to locate all lesions. Some may be
quite small and difficult to find. Likewise we will employ brain MRIs and CTs to
locate small tumors (usually metastatic lesions) that are also difficult to find.
Small tumors may become large tumors and it is therapeutically important to find
the lesions when they are still small.
OUR APPROACH
We believe that teaching is a process that involves an active partnership. Our role
is that of a guide to your learning. Therefore, we are responsible to open the way,
to encourage, and to nudge you toward your own learning. In the context of the
math clinic, we will try to model the process of applying mathematics to the
medical image analysis. We will help guide you toward this learning by providing
mathematics for you to experience. It is our aim to communicate mathematics in
a way that is supportive and nurturing of your efforts. Your role is to find a way to
experience and articulate the mathematics that is presented and that you
encounter. We believe that it is your responsibility to let us know when you find
yourself not understanding mathematical concepts that are presented in class.
Once you make this known, it is our responsibility to work on trying to attain
clarity. We will try to be as proactive as possible. We believe that results on
projects give us the opportunity to clearly see where the areas of mathematical
understanding are and what areas need more attention.
OUTCOMES
By the end of the semester you should be able to read, understand, and apply
appropriate methods associated with aspects of medical image analysis we’ve
studied this semester to model correctly and to solve associated problems.
Secondly, given a medical image analysis problem, you should be able to: (i)
translate the description of the problem into an algorithm, (ii) choose and apply
the appropriate software method(s), (iii) obtain the an appropriate solution(s), and
(iv) (correctly) interpret and display results. Lastly, by the end of the semester
you should be able to judge, for yourself, the veracity of statements made in the
areas of our study.
EVALUATION
Each person on a team will execute a project (identify a set of problems, find
solution methods, present the results and write-up the results). Individualization
of grades are based on the adequacy of fulfillment of the division of labor
associated with your team’s project proposal. In particular, the following are
components that will be evaluated.
1. Participation – attendance and contributing to class interactions and
discussions (10%)
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2. Annotated bibliography – Included in the Progress Report (5%)
3. Progress Report (10%)
a. Write-up (5%)
b. Presentation (5%)
4. Final Presentation (10%)
5. Final written report (20%)
6. Final software (15 %)
7. Assignments (20% total)
8. Implementation (10%)
** Graduate students will have extended content and be held to higher standards.
The grade assignments are on the 10 percent scale (A = 90%-100%, B = 80%89%, C = 70%-79%, D = 60-69%).
General advice: Keep all materials that we turn back in case you think we have
not credited you with the points you earned. we can only correct your score if
you have what we have turned back to you. It is a good idea to xerox anything
that you turn in just in case we lose what you turn in. Please check to make sure
that the points you earned are the points we have recorded. Note: The statistics
that we have read about correctness of professors in recording grades state that
there is a 6% error rate in our recording of your grades. Please make sure that we
have correctly recorded your points.
POLICIES
Legitimate Excuses: Legitimate excuses are for reasons that are beyond your
control. You may be required to produce an official, signed excuse. If you are
needed in a wedding, for example, you must talk to us prior to the (blessed)
event. If you are legally arrested, then this is not a legitimate excuse. For matters
that are within your control, the general rule is that it is not excused. However,
talk to us prior to the event or arrest or departure.
ANNOTATED BIBLIOGRAPHY
An annotated bibliography is a full bibliographical list of citations of the materials
you have read and consulted with notes. The notes you write are more or less
notes to yourself or to someone next semester that may take up your project. In
general you should have at least five citations and the annotation should state
whether or not you found it useful to your project and why.
INSTRUCTIONS FOR PROJECTS
A project consists of:
1. Proposal – Depending on the size of the class, projects may be coalesced
or deleted. Each proposed project will be divided into tasks and assigned
to each group so that the assignment is equitable. These tasks and
assignments need to be written up and submitted to us. Once the tasks
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2.
3.
4.
5.
6.
have been identified, assigned, and approved, a division of labor is written
by each of the groups.
Division of labor – Each group must take their tasks and subdivide them
into subtasks that are assigned to individuals in the group with an
associated due-date. A division of labor is a formal contract between the
members of the group. Once the tasks have been approved and a written
division of labor submitted, the group needs to schedule a meeting with us
so that we can go over the division of labor, its associated responsibilities
and expectations.
Methods and Materials – is a description of what materials (software)
and the approach(es) (methods, algorithms) you will use.
Annotated Bibliography
Software
a. Code - the actual computer implementation of the project.
Attention must be paid to efficiency, readability, and portability.
b. Input – the way information is passed to the software must be
transparent and easily usable by the client.
c. Execution - the algorithm as run must correctly perform what it
was designed to do.
d. Output - relevant, clear display of solution(s) such as tables,
graphs, images, reports/lists.
e. Ease – ease of use.
f. Documentation – an in-line and hardcopy of the documentation
on how to use the software needs to be written. Moreover, help
files must be part of the software.
Testing and analysis
a. Testing - this part in the context of our clinic consists of running
the software developed on the test problems.
b. Analysis - the purpose of an analysis is to get you to critically
evaluate the results obtained from the software as it was run on the
test problems. Part of an analysis is a critique of the software.
* A caveat: Negative results are not prohibited. Negative results can be very
valuable. However the negative results must be robust in that it would be
novel and instructive to the expert community to avoid a particular pathway.
7. Report that will be a chapter in the Clinic Report – Each team will need
to be responsible for parts of the final report. This will be done in MSWord (needs to be translatable into PDF-Adobe) or Latex as long as we
can merge the files in Latex or Adobe Acrobat. The final report will
(subject to modifications we uncover) consist of:
a. Introduction – clinic instructors
b. Project 1
i. Theoretical foundations – theory, application, algorithms
ii. Software – description
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iii. Results – summary, tables, graphs, images, lists,
distinguishing features, performance, and limitations
iv. Opportunities for further research
v. Conclusions
vi. Bibliography
vii. Appendices
 Source code
 Test problems, data, example runs
 Documentation
 Proofs
 Other
c. Project 2 (same as Project 1) …
d. Project N (same as Project 1)
NOTE: The software from all teams needs to be burned onto one disk where each
team will have a directory into which the software is stored. At the head of each
team’s directory entry, there must be a “read-me” file and the software system
must have a “help” command that assists the user.
Note: Each problem from your problems sets will be worth the same 1/22nd of
20% for undergrads and 1/29th of 20% for graduates.
PROBLEM SET 1: Due February 12th at 5pm
E2.3, E2.4, E3.1,E4.2,E4.3,E4.4,E4.7,E4.8
Graduate Students add E4.5, E4.9
PROBLEM SET 2: Due March 12th at 5pm
E7.1, E7.4,
E11.6, E11.7, E11.8, E11.11,
E13.1,E13.2,E13.6,E13.9
Graduate Students add E7.5, E7.7, E11.9
PROBLEM SET 3: Due April 16th at 5pm
E16.1, E16.2, E16.7, E16.8
Graduate Students add E16.4, E16.5
IMPLEMENTATIONS OF THE SEMEION APPROACH:
Team 1, Write a MATLAB implementation or use the Semeion software for JNet. Due April 9th at 5pm, class presentation April 12th
Teams 2 and 4, Write a MATLAB implementation or use the Semeion software,
Auto CM (half the teams), probabilistic ANNs (the other half of the teams). Due
April 16th at 5pm , class presentation April 19th
Team 3, Write a MATLAB implementation or use the Semeion software for
ACM. Due April 23rd at 5pm, class presentation April 26th
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