Knowledge Systems Lab JN 9/15/2015 Heterogeneous Collection of Learning Systems for Confident Pattern Recognition Joshua R. New Knowledge Systems Laboratory Jacksonville State University

Knowledge Systems Lab JN 9/15/2015 Outline Motivation Simplified Fuzzy ARTMAP (SFAM) Interactive Learning Interface System Demonstration Conclusions and Future Work

Knowledge Systems Lab JN 9/15/2015 Motivation

Knowledge Systems Lab JN 9/15/2015 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 9/15/2015 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 9/15/2015 Simplified Fuzzy ARTMAP (SFAM)

Knowledge Systems Lab JN 9/15/2015 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 9/15/2015 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 9/15/2015 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 9/15/2015 SFAM Vector-based graphic visualization of learning Array of Pixel Values x y Category members Category member Category members

Knowledge Systems Lab JN 9/15/2015 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)

Knowledge Systems Lab JN 9/15/2015 SFAM SFAM is sensitive to the order of the inputs x y Category members Category member Category members Vector 3 Vector 1 Vector 2

Knowledge Systems Lab JN 9/15/2015 SFAM Voting scheme of 5 Heterogeneous SFAM networks to overcome vigilance and input order dependence –3 networks: random input order, set vigilance –2 networks: 3 rd network order, vigilance ± 10%

Knowledge Systems Lab JN 9/15/2015 SFAM

Knowledge Systems Lab JN 9/15/2015 SFAM Threshold results Overlay results Trans-slice results

Knowledge Systems Lab JN 9/15/2015 Interactive Learning Interface Screenshot of Segmentation & Features

Knowledge Systems Lab JN 9/15/2015 System Demonstration

Knowledge Systems Lab JN 9/15/2015 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 9/15/2015 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