incorporating artificial intelligence into mammography prediction louis oliphant computer sciences...
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![Page 1: Incorporating Artificial Intelligence into Mammography Prediction Louis Oliphant Computer Sciences Department University of Wisconsin-Madison](https://reader035.vdocuments.site/reader035/viewer/2022062410/5697bff91a28abf838cbff58/html5/thumbnails/1.jpg)
Incorporating Artificial Intelligence into Mammography Prediction
Louis OliphantComputer Sciences DepartmentUniversity of Wisconsin-Madison
![Page 2: Incorporating Artificial Intelligence into Mammography Prediction Louis Oliphant Computer Sciences Department University of Wisconsin-Madison](https://reader035.vdocuments.site/reader035/viewer/2022062410/5697bff91a28abf838cbff58/html5/thumbnails/2.jpg)
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.
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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
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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
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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
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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).
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Our Model
Rules + TAN
Prolog + Java
Do: Find rule and add it if improves model performanceUntil time limit
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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
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Birads >= 5
Birads >= 4
Birads…
TAN
TAN + Rules
Our Results
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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
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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