Incorporating Artificial Intelligence into Mammography Prediction

Louis OliphantComputer Sciences DepartmentUniversity of Wisconsin-Madison

What is Artificial Intelligence?

The study and design of intelligent agents

Poole, Mackworth & Goebel 1998 http://www.bostondynamics.com/content/sec.php?section=BigDog GeneScan, C. Burge and S. Karlin 1997http://picasa.google.com/ http://www.toshiba.co.jp/about/press/2005_05/pr2001.htmhttp://babelfish.yahoo.com/

the spirit indeed is willing,but the flesh is weak.

De geest is wel gewillig,maar het vlees is zwak.

Y

0

0

1

0

0

0

Prediction:X

9 8 6 5 4 5 3 4 6

2 9 8 6 4 3 2 1 1

2 1 3 4 5 6 4 7 8

9 0 0 9 5 8 4 2 4

6 7 9 0 4 6 2 7 9

0 2 1 1 1 2 4 6 6

Prediction: Y

1

X

6 7 5 4 9 4 2 2 1

Supervised Machine Learning

X Y

1 3 2 4 1 1 8 3 3 0

2 3 2 4 6 2 1 7 2 0

4 5 6 7 7 7 2 1 7 1

4 5 6 2 1 7 8 2 6 0

3 2 1 1 1 0 4 3 3 1

5 4 3 1 6 4 7 3 2 0

7 8 6 7 5 3 4 1 7 0

5 6 7 7 4 2 3 1 1 1

0 9 8 9 7 4 6 3 2 0

Training Data

Classifier ModelTrained Classifier Model

Test DataY

0

0

1

0

0

1

Accuracy0.87

Nearest NeighborNeural NetworkSupport Vector Machine

X Y

1 3 2 4 1 1 8 3 3 0

2 3 2 4 6 2 1 7 2 0

4 5 6 7 7 7 2 1 7 1

4 5 6 2 1 7 8 2 6 0

3 2 1 1 1 0 4 3 3 1

5 4 3 1 6 4 7 3 2 0

7 8 6 7 5 3 4 1 7 0

5 6 7 7 4 2 3 1 1 1

0 9 8 9 7 4 6 3 2 0

Fixed Length Feature Vector

Fixed Length Feature Vector

First Order Logic Data

X y

1 3 2 4 1 1 8 3 3 0

2 3 2 4 6 2 1 7 2 0

4 5 6 7 7 7 2 1 7 1

4 5 6 2 1 7 8 2 6 0

3 2 1 1 1 0 4 3 3 1

5 4 3 1 6 4 7 3 2 0

7 8 6 7 5 3 4 1 7 0

5 6 7 7 4 2 3 1 1 1

0 9 8 9 7 4 6 3 2 0

Id Patient Date MassShape

… MassSize

Loc

1 P1 5/03 Oval 3mm RU4

2 P1 5/04 Round 8mm RU4

3 P2 5/04 Oval 4mm LL3

4 P3 6/00 Round 2mm RL2

… … … … … …

Mammography Dataset

Birads

3

5

1

4

…

Malignant/Benign

M

M

B

B

…

Collected from April 1999 to February 200418,270 Patients47,669 Mammograms510 Malignant 61,709 Benign

Inductive Logic Programming

Growth Medium: soil, woodCap Color: white, redGrouping: single, clusterAnnulus: present, not present

edible poisonous

edible(X) :- cap_color(X,red), annulus(X,present).

edible(X) :- medium(X,wood), grouping(X,single).

Our Model

Rules + TAN

Prolog + Java

Do: Find rule and add it if improves model performanceUntil time limit

Testing The Model

Purpose: assess future performance

Data set

Train set Test set

Score: 0.67

Small test setHigh variance

Cross Validation

Train setTest set

Score: 0.68

Score: 0.65

Score: 0.79

Score: 0.81

Average Score:0.72

Standard Deviation:0.07

Birads >= 5

Birads >= 4

Birads…

TAN

TAN + Rules

Our Results

Fielding The System

Approval process for pilot study Web page interface

XHTML, Javascript Enter Descriptors, Patient profile,

Radiologist’s Score Backend

Java + Prolog Return probability of malignancy and

Why model makes the prediction

Conclusions

Computers good at combining multiple interacting features

Adding rules improves performance Rules lend insight into predictive models

To Remember

Supervised Machine Learning Data set Model Evaluation Metric