knowledge systems lab jn 9/15/2015 heterogeneous collection of learning systems for confident...

JN 04/21/23

Knowledge Systems Lab

Heterogeneous Collection of Learning Systems for Confident

Pattern Recognition

Joshua R. NewKnowledge Systems Laboratory

Jacksonville State University

JN 04/21/23


Outline

• Motivation

• Simplified Fuzzy ARTMAP (SFAM)

• Interactive Learning Interface

• System Demonstration

• Conclusions and Future Work

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Motivation

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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?

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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

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Simplified Fuzzy ARTMAP (SFAM)

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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)

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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

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SFAM

• Data from which to learn– Feature vector from slice pixel values from shunted and single-

opponency images (Whole Brain Atlas)

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SFAM

• Vector-based graphic visualization of learning

Array of Pixel Values

x

y

Category 1 - 2 members

Category 2 - 1 member


0.35 0.90

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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

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SFAM

• SFAM is sensitive to the order of the inputs

x

y


Category 2 - 1 member


Vector 3

Vector 1

Vector 2

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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%

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SFAM

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SFAM

Threshold results

Overlay results

Trans-slice results

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Interactive Learning Interface

• Screenshot of Segmentation & Features

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System Demonstration

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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

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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 9/15/2015 heterogeneous collection of learning systems for confident...

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