Heterogeneous collection of learning systems for confident pattern recognition
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Heterogeneous Collection of Learning Systems for Confident Pattern Recognition. Joshua R. New Knowledge Systems Laboratory Jacksonville State University. Outline. Motivation Simplified Fuzzy ARTMAP (SFAM) Interactive Learning Interface System Demonstration Conclusions and Future Work.

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Heterogeneous collection of learning systems for confident pattern recognition l.jpg

Heterogeneous Collection of Learning Systems for Confident Pattern Recognition

Joshua R. New

Knowledge Systems Laboratory

Jacksonville State University


Outline l.jpg
Outline Pattern Recognition

  • Motivation

  • Simplified Fuzzy ARTMAP (SFAM)

  • Interactive Learning Interface

  • System Demonstration

  • Conclusions and Future Work


Motivation l.jpg
Motivation Pattern Recognition


Motivation4 l.jpg
Motivation Pattern Recognition

  • 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?


Motivation5 l.jpg
Motivation Pattern Recognition

  • 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


Simplified fuzzy artmap sfam l.jpg
Simplified Fuzzy ARTMAP (SFAM) Pattern Recognition


Slide7 l.jpg
SFAM Pattern Recognition

  • 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)


Slide8 l.jpg
SFAM Pattern Recognition

  • 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


Slide9 l.jpg
SFAM Pattern Recognition

  • Data from which to learn

    • Feature vector from slice pixel values from shunted and single-opponency images (Whole Brain Atlas)


Slide10 l.jpg

0.35 Pattern Recognition

0.90

Category 1 - 2 members

Category 2 - 1 member

y

Category 4 - 3 members

x

SFAM

  • Vector-based graphic visualization of learning

Array of Pixel Values


Slide11 l.jpg
SFAM Pattern Recognition

  • 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


Slide12 l.jpg

Category 1 - 2 members Pattern Recognition

Category 2 - 1 member

y

Category 4 - 3 members

x

Vector 1

Vector 2

Vector 3

SFAM

  • SFAM is sensitive to the order of the inputs


Slide13 l.jpg
SFAM Pattern Recognition

  • 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%


Slide14 l.jpg
SFAM Pattern Recognition


Slide15 l.jpg
SFAM Pattern Recognition

Threshold results

Trans-slice results

Overlay results


Interactive learning interface l.jpg
Interactive Learning Interface Pattern Recognition

  • Screenshot of Segmentation & Features


System demonstration l.jpg
System Demonstration Pattern Recognition


Conclusions l.jpg
Conclusions Pattern Recognition

  • 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


Future work l.jpg
Future Work Pattern Recognition

  • 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


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