Cancer diagnosis is essentially a human task. Almost universally, the process requires the extraction of tissue (biopsy) and examination of its microstructure by a human. To improve diagnoses based on limited and inconsistent morphologic knowledge, a new approach has recently been proposed that uses molecular spectroscopic imaging to utilize microscopic chemical composition for diagnoses. In contrast to visible imaging, the approach results in very large data sets as each pixel contains the entire molecular vibrational spectroscopy data from all chemical species. Here, we propose data handling and analysis strategies to allow computer-based diagnosis of human prostate cancer by applying a novel genetics-based machine learning technique ({\tt NAX}). We apply this technique to demonstrate both fast learning and accurate classification that, additionally, scales well with parallelization. Preliminary results demonstrate that this approach can improve current clinical practice in diagnosing prostate cancer.

1. Towards Better than Human Capability in
Diagnosing Prostate Cancer
Using Infrared Spectroscopic Imaging
Xavier Llorà1, Rohith Reddy2,3, Brian Matesic2, Rohit Bhargava2,3
1 National Center for Supercomputing Applications & Illinois Genetic Algorithms Laboratory
2 Department of Bioengineering
3 Beckman Institute for Advanced Science and Technology
University of Illinois at Urbana-Champaign
Supported by AFOSR FA9550-06-1-0370, NSF at ISS-02-09199
DoD W81XWH-07-PRCP-NIA and the Faculty Fellows program at NCSA
GECCO 2007 HUMIES 1

2. Motivation
• The American Cancer Society estimated 234,460 new cases of
prostate cancer in 2006.
• Screening test:
– Digital rectal examination
– Prostate specific antigen (PSA) level
• Suspicious patients undergo biopsy process
• 1 million people undergo biopsies in the US alone per year
• Pathologist diagnose
– Crucial for the therapy
– Human accuracy ( error < 5% )
– Costs
GECCO 2007 Llorà, Reddy, Matesic & Bhargava 2

3. Current Diagnosis Procedure
• Biopsy-staining-microscopy-manual recognition is the diagnosis
procedure for the last 150 years.
GECCO 2007 Llorà, Reddy, Matesic & Bhargava 3

4. Advances on Fourier Transform IR Imaging
• Infrared spectroscopy is a classical technique for
measuring chemical composition of specimens.
• At specific frequencies, the vibrational modes of
molecules are resonant with the frequency of infrared
light.
• Microscope has develop to the point that resolution
that match a pixel with a cell (and keep improving).
• It allows to start from the same data (stained tissue)
• Generates larges volumes of data
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5. Advances on Fourier Transform IR Imaging
GECCO 2007 Llorà, Reddy, Matesic & Bhargava 5

6. Spectrum Analysis
• Microscope generate a lot of data
• Per spot the spectra signature requires GBs of storage
• Bhargava et al. (2005) feature extraction for tissue identification
• More than 200 potential features per spectrum (cell/pixel)
• Firsts methodology that allowed tissue identification
GECCO 2007 Llorà, Reddy, Matesic & Bhargava 6

7. Human Activity
• As mentioned earlier: Area of exclusive human activity
• Two key tasks:
– Using the spectra identify tissue type
– Using filtered tissue diagnose samples
• Both tasks:
– Require learning
– Can be model as supervised learning problems
• Challenges:
– Very large volumes of information
– Scalability and efficiency is a priority
– Interpretability of the models
GECCO 2007 Llorà, Reddy, Matesic & Bhargava 7

8. Genetics-Based Machine Learning
• GA-driven learning mechanisms
• Mainly rule based models
• Pittsburgh approach
• Inherently parallel process
• GBML is a good candidate for very large problems
• Rule matching is know to be the governing factor on
the execution time (Llorà & Sastry, 2006)
GECCO 2007 Llorà, Reddy, Matesic & Bhargava 8

9. Current Off-the-Shelf Systems
• There is a wide variety of GBML/LCS implementations
• Most of them:
– Oriented to run experiments in a single processors
– Have large memory footprints
– Typical problem = tens of attributes + thousand
records
– Few attention to efficient implementation and
acceleration techniques (Llorà & Sastry, 2006)
• Cancer diagnosis overwhelms them:
– Hundreds of features
– Millions of records
GECCO 2007 Llorà, Reddy, Matesic & Bhargava 9

10. NAX Specs
• Affordable memory footprints
• Squeeze any computation you got
• Efficient implementations:
– Hardware acceleration
– Massive parallelism
GECCO 2007 Llorà, Reddy, Matesic & Bhargava 10

11. NAX Mechanics
• The basic procedure:
1. Create an empty decision list
2. GA evolves a maximally accurate and maximally
general rule using the available instances
3. Add the evolved rule to the decision list
4. Remove all the instances covered by the rule
5. If there are uncovered instances go to step 2
GECCO 2007 Llorà, Reddy, Matesic & Bhargava 11

12. A Little Story about Hardware
• SIMD (Single Instruction Multiple Data) architectures were hot
in the ‘80s supercomputing scene
• SIMD were widely used to performed binary operations among
two vector operands (Cray)
• Those processors were very expensive
• Consumer products took another path, the scalar one
– No SIMD support in hardware (left to the software)
– The massive with spread of needs for CPUs make them cheaper
and cheaper
• Side effect:
– Hot in the supercomputing scene in the ‘90s become building
machines with large numbers of “cheap” processors
GECCO 2007 Llorà, Reddy, Matesic & Bhargava 12

13. The Consumer Market Strikes Back
• Computer games and multimedia applications
– Use a particular type of matrix operations
– Graphics heavily use 4x4 matrix operations
– Digital signal processing applications also take advantage of it
• In late ‘90s Intel introduced SIMD instructions on Pentium chips via
MMX
– Multimedia oriented instructions
– Vector operations for fix-size blocks
– Goal: accelerate via hardware multimedia apps
• Nowadays most vendors provide “multimedia” vector instruction
sets
– Intel: MMX, SSE, SSE2, SSE3
– AMD: 3Dnow!, 3Dnow+! (also support Intel’s MMX, SSE, SSE2)
– IBM/Motorola: AltiVec
GECCO 2007 Llorà, Reddy, Matesic & Bhargava 13

14. A Simple Example (I/II)
• Match = a simple aligned ‘and’ and ‘equal’
Instance Instance
10 01 10 01 01 10 10 01
0101 1001
Condition Condition
& &
10 01 11 11 10 01 11 11
01## 01##
Temp Temp
10 01 10 01 00 00 10 01
== ==
Instance Instance
10 01 10 01 01 10 10 01
Matched Not Matched
11 11 11 11 10 01 11 11
• Vector operations allow different manipulations
• 4 floats can be manipulated at once (spectra features)
GECCO 2007 Llorà, Reddy, Matesic & Bhargava 14

15. A Simple Example (II/II)
1
2
OP1
3
4
1
2
OP2
3
4
1
4
Res
9
16
vecOP1 vecOP2
1 2 3 4 1 2 3 4
vecRes 1 4 9 16
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16. Exploiting the Inherent Parallelism
• Rule matching rules the overall execution time
• Fitness calculation > 99%
• The parallelization method focused on reducing
communication cost
• The idea
– Most of the time evaluating
– Evaluate the evaluation
– No master/slave
– All processors run the same GA seeded in the same manner
– Each processor only evaluate a chunk of the population (N/p)
– Broadcast the fitness of the chunk to the other processors
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17. NAX: Stretching GBML
GECCO 2007 Llorà, Reddy, Matesic & Bhargava 17

18. Prostate Cancer Data
1. Tissue identification
– Modeled as a supervised learning problem
– (Features, tissue type)
– The goal: Accurately retrieve epithelial tissue
2. Tissue identification
– Modeled as a supervised learning problem
– (Features, diagnosis)
– The goal: Accurately diagnose each cell (pixel) and
aggregate those diagnosis to generate a spot
(patient) diagnosis
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19. GBML Identifies Tissue Types Accurately
Original
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20. GBML Identifies Tissue Types Accurately
OK
Misclassified
• Accuracy >96%
• Mistakes on minority classes (not targeted) and boundaries
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21. Filtered Tissue is Accurately Diagnosed
Original
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22. Filtered Tissue is Accurately Diagnosed
Diagnosed
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23. Filtered Tissue is Accurately Diagnosed
• Pixel crossvalidation accuracy (87.34%)
• Spot accuracy
– 68 of 69 malignant spots
– 70 of 71 benign spots
• Human-competitive computer-aided diagnosis system
is possible
• First published results that fall in the range of
human error (<5%)
GECCO 2007 HUMIES Llorà, Reddy, Matesic & Bhargava 23

24. Breakthrough
• Current best published result, examples from
different fields
– Image Analysis - 77% accuracy1 (cancer/no cancer)
– Raman Spectroscopy – 86%2 accuracy
– Genomic analysis – 76% (low grade/high grade cancer)
1. R. Stotzka et al. Anal. Quant. Cytol. Histol.,17, 204-218 (1995).
2. P. Crow et al. Urol. 65, 1126-1130 (2005)
3. L. True et al. Proc Natl Acad Sci U S A. 2006 Jul 18;103(29):10991-10996.
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25. Conclusions
• Humans are the ultimate and only source of diagnosis
• The FTIR imaging provides information about chemical
signatures and structure
• Large volumes of data forced efficient GBML design
• Diagnosis require two steps
• The results on prostate cancer are human competitive
• No previous method has been able to match
pathologist accuracy
GECCO 2007 Llorà, Reddy, Matesic & Bhargava 25

26. Towards Better than Human Capability in
Diagnosing Prostate Cancer
Using Infrared Spectroscopic Imaging
Xavier Llorà1, Rohith Reddy2,3, Brian Matesic2, Rohit Bhargava2,3
1 National Center for Supercomputing Applications & Illinois Genetic Algorithms Laboratory
2 Department of Bioengineering
3 Beckman Institute for Advanced Science and Technology
University of Illinois at Urbana-Champaign
Supported by AFOSR FA9550-06-1-0370, NSF at ISS-02-09199
DoD W81XWH-07-PRCP-NIA and the Faculty Fellows program at NCSA
GECCO 2007 HUMIES 26