Heterogeneous Collection of
Learning Systems for Confident
Pattern Recognition
Joshua R. New
Knowledge Systems Laboratory
Jacksonville State University
Knowledge Systems Lab
JN 7/13/2017
Outline
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Motivation
Simplified Fuzzy ARTMAP (SFAM)
Interactive Learning Interface
System Demonstration
Conclusions and Future Work
Knowledge Systems Lab
JN 7/13/2017
Motivation
Knowledge Systems Lab
JN 7/13/2017
Motivation
• Doctors and radiologists spend several
hours daily analyzing patient images (ie.
MRI scans of the brain)
• The patterns being searched for in the
image are standard and well-known to
doctors
• Why not have the doctor teach the
computer to find these patterns in the
images?
Knowledge Systems Lab
JN 7/13/2017
Motivation
• Doctors and radiologists who use supervised
AI systems for image segmentation:
– Usually can not interactively refine the computer’s
segmentation performance
– Must be able to precisely select regions/pixels of
the image to train the computer
– Often do not use an interface that facilitates
accomplishment of their task
– Can easily lose where they are looking in the
image when using magnification
Knowledge Systems Lab
JN 7/13/2017
Simplified Fuzzy ARTMAP (SFAM)
Knowledge Systems Lab
JN 7/13/2017
SFAM
• In order to “teach the computer” to find
tumors in neuro-images, a supervised
machine learning system must be used
• Simplified Fuzzy ARTMAP (SFAM) is a neural
network that was created by Grossberg in
1987 and uses a mathematical model of the
way the human brain learns and encodes
information
• This AI system was utilized because it allows
very fast learning for interactive training (ie.
seconds instead of days to weeks)
Knowledge Systems Lab
JN 7/13/2017
SFAM
• SFAM is a computer-based system
capable of online, incremental learning
• Two “vectors” are sent to this system for
learning:
– Input feature vector gives the data is
available from which to learn
– Supervisory signal indicates whether that
vector is an example or counterexample
Knowledge Systems Lab
JN 7/13/2017
SFAM
• Data from which to learn
– Feature vector from slice pixel values from shunted and
single-opponency images (Whole Brain Atlas)
Knowledge Systems Lab
JN 7/13/2017
SFAM
• Vector-based graphic visualization of learning
Category 1 - 2 members
y
Category 2 - 1 member
Array of
Pixel Values
Category 4 - 3 members
0.35
0.90
x
Knowledge Systems Lab
JN 7/13/2017
SFAM
• Only one tunable parameter – vigilance
– Vigilance can be set from 0 to 1 and corresponds to the
generality by which things are classified
(ie. vig=0.3=>human, vig=0.6=>male, 0.9=>Joshua New)
0.675
0.75
0.825
Knowledge Systems Lab
JN 7/13/2017
SFAM
• SFAM is sensitive to the order of the inputs
Vector 1
Category 1 - 2 members
Vector 2
Category 2 - 1 member
y
Vector 3
Category 4 - 3 members
x
JN 7/13/2017
Knowledge Systems Lab
SFAM
• Voting scheme of 5 Heterogeneous
SFAM networks to overcome vigilance
and input order dependence
– 3 networks: random input order, set vigilance
– 2 networks: 3rd network order, vigilance ± 10%
Knowledge Systems Lab
JN 7/13/2017
SFAM
Knowledge Systems Lab
JN 7/13/2017
SFAM
Threshold results
Trans-slice results
Overlay results
Knowledge Systems Lab
JN 7/13/2017
Interactive Learning Interface
• Screenshot of Segmentation & Features
Knowledge Systems Lab
JN 7/13/2017
System Demonstration
Knowledge Systems Lab
JN 7/13/2017
Conclusions
• Doctors and radiologists can teach the
computer to recognize abnormal brain tissue
• They can refine the learning systems results
interactively
• They can precisely select targets/non-targets
• They can zoom for precision while
maintaining context of the entire image
• The interface developed facilitates task
performance through display of segmentation
results and interactive training
Knowledge Systems Lab
JN 7/13/2017
Future Work
• Quantity of health-care can be increased by
utilizing these trained “agents” to allow
radiologists to only view the required images
and directing their attention for the ones that
are viewed
• Quality of health care can be increased by
using the agents to classify an entire
database of images to highlight possibly
overlooked or misdiagnosed cases
Knowledge Systems Lab
JN 7/13/2017