UC Berkeley Data Dialogs Conference Talk (09.07.16) - Predicting Breast Cancer Proliferation Scores with Apache SystemML Talk page: https://datadialogs.ischool.berkeley.edu/2016/schedule/predicting-breast-cancer-proliferation-scores-apache-spark-and-apache-systemml Video: https://www.youtube.com/watch?v=k4WrmEx2DNg

1. Predicting Breast
Cancer
Proliferation
Scores with
Apache SystemML
Mike Dusenberry & Madison J Myers
IBM Spark Technology Center, SF

2. Let us introduce ourselves.

3. We like health and we like data.

4. Found a
Challenge

5. Breast Cancer Tumor Proliferation
Challenge
● Images of Tumors: Can be analyzed and given a
score for medical assessment.
● Tumor Score: Difficult to determine and takes a
trained eye.
● Currently assessed by Pathologists (M.D./D.O.).
● Dataset contains 500 images of breast cancer tissue,
each at more than 15GB.

6. Context
•Breast cancer is a leading cause of cancerous death in women.
•Survival rates increase as early detection increases, incentivizing
quicker detection.
•Tumor cell proliferation is a strong indicator of a patient’s
prognosis.
•Currently, pathologists classify tumors based on proliferation by
counting the dividing cell nuclei in hematoxylin & eosin stained
slides by hand with a microscope.
•Suffers due to underlying subjectivity.

7. Example
Image:

8. Example
Zoom-In of
Image
Looking for
Nuclei
Characteristics

9. Grading System in Invasive Breast Cancer

10. Goal:
Predict tumor scores from slide images.

11. Okay easy,
where do we
start?

12. Blockers:

13. 1) Using really, really large images

14. 1. Using really, really large images
2. Limited number of images

15. 1. Using really, really large images
2. Limited number of images
3. Current state of the art model for
this type of task is a deep CNN

16. Seriously,
where do we
start?

17. Reference Paper:
“Automated Grading of Gliomas
using Deep Learning in Digital
Pathology Images”
Daniel L. Rubin, MD, MS Lab
Department of Radiology &
Department of Medicine
(Biomedical Informatics Research),
Stanford University

18. “Automated Grading of Gliomas using Deep
Learning in Digital Pathology Images”
1. Cut a “whole-slide” image into square “tiles” at 20x magnification.
2. Filter the “tiles” to remove any without tissue.
3. Cut the remaining “tiles” into smaller “samples”.
4. Assign a tumor score label to each sample based on the tumor score of the
“whole-slide” image.
5. Repeat 1-4 for all “whole-slide” images.
6. Train a convolutional neural network with the resulting dataset of labeled
“samples”.
7. Good results!

19. 20 slides vs 500 slides

20. … we have lots of data

21. Our Approach:
● Utilize Apache Spark to cut and filter all 500
labeled, extremely high-resolution tumor slide
images into 4.7 million smaller square
samples.
● Utilize Apache SystemML on top of Spark to
train a convolutional neural network on the
labeled samples.

22. After
preprocessing,
over 7 terabytes of
data...

23. What is Apache Spark?
● Apache Spark is a fast and general engine for large-scale data
processing.
● Combines ML, SQL, streaming, and other complex analytics.
● Extends Scala idioms, as well as R/Python DataFrame idioms to
cluster computing.
● APIs for Scala, Java, Python, R.
● Simple to use!
● Much more information
at https://spark.apache.org/.

25. What is Apache SystemML?
● Apache SystemML is a machine learning system for running
distributed linear algebra on top of Apache Spark.
● Exposes high-level R-like & Python-like languages focused on
linear algebra.
● APIs for Python, Scala, Java.
● Much more information
at http://systemml.apache.org/.

27. Preprocessing

28. Preprocessing Approach at a High Level
“Whole-Slide” Image Image Tiles
1024x1024x3
pixel tiles

29. Image Tiles
Preprocessing Approach at a High Level (cont.)
Example“Tile” Image
(1024x1024x3)

30. Now that we have
tiles, we need to filter
out non-tissue. We
did this with
thresholding.

31. % of breast cancer
tissue in image
After
applying
thresholding
to tiles to
close small
gaps and
adipose
tissue:
If >= 90%,
we keep the
tile.

32. Preprocessing Approach at a High Level (cont.)
Example “Sample”
Image (256x256x3)
Image Tiles
Example Filtered
“Tile” Image Tile Samples

33. Preprocessing
Code
Example:

34. Now for the
good
stuff……
Machine
Learning

35. What are CNNs, or
Convolutional
Neural Networks?

36. ● Deep Learning
model
● State of the art
for computer
vision tasks
● and audio….
● and….

37. Example Convolutional Neural Network

38. Large data,
remember?

39. Apache SystemML & Apache Spark

40. Breast Cancer
ConvNet w/
SystemML

41. Training ConvNet w/ PySpark API

42. Entire Pipeline Diagram
Example
“Sample” Image
Image Tiles
Example Filtered
“Tile” Image Tile Samples“Whole-Slide” Image
ConvNet:
“Tumor
Proliferation
Score”

43. Thank You

44. Backup

46. % of breast cancer
tissue in image
After
applying
thresholding
to tiles to
close small
gaps and
adipose
tissue:
If >= 90%,
we keep the
tile.

47. Where are we
now?

48. ● Preprocessing: Complete
● Machine Learning:
○ Small-scale tests complete
○ Large-scale tests in progress.