This presentation looks at the benefits and problems related to computer aided diagnosis in pathology. It was delivered by Dr. Liron Pantanowitz, University of Pittsburgh, USA at the Pathology Horizons conference in Cairns, Australia. Pathology Horizons is an annual CPD conference organised by Cirdan on the future of pathology. More information on Pathology Horizons can be accessed at www.pathologyhorizons.com

1. Computer Aided Diagnosis in
Pathology: Pros & Cons
Liron Pantanowitz MD
Director of Pathology Informatics & Cytology
Professor of Pathology & Biomedical Informatics
University of Pittsburgh Medical Center (UPMC)
pantanowitzl@upmc.edu

2. Disclosure
Consultant for Hamamatsu

3. Objectives
Overview of CAD in Pathology
 Pros & cons of image analysis
Recommendations for the field

4. AI General Facts
• AI is no longer just a science-fiction Hollywood story.
• 61% of people see AI as making the world a better place.
• 55% of people would trust using a self-driving car.
• 57% would prefer an AI doctor to perform an eye exam.
• Major investments in AI predicted to grow 300% in 2017.
• Workers do fear they could be replaced by machines!

5. AI in Healthcare
• Use of algorithms & software to
approximate human cognition to
analyze complex medical data.
• Early phases used fuzzy logic,
Bayesian networks & artificial
neural networks.
• Improved computing, EHRs,
growth of health-related data +
computer vision.
• Recent projects include Google
DeepMind & IBM Watson.

6. Google uses AI to detect
lymph node metastatic
breast carcinoma

7. Image Analysis Trends
• Business analyses predict software is high-growth market
• New players entering the market post FDA-approval of WSI
• Image analysis is indeed the “holy grail” of digital pathology
• Transition from qualitative (descriptive) to quantitative science
• Precision medicine currently demands precision diagnostics
• Current shift from research to more useful clinical applications
• Much hype surrounding automated Computer Aided Diagnosis

8. Computational Pathology
Computational Pathology
Big Data + Images
Computer-Aided Diagnosis
Deep Learning Image Analysis
Computation to interpret
multi-parameter data
To Assist (“Replace”)
Pathologists
C
D
S
±
MLCNN
AI

9. Why use image analysis?
Edward Adelson checkershadow illusion
Perception of color (human limitation)

10. Deep Learning for HER2
• Diagnostic discordance was caused by perception differences in
assessing HER2 due to stain heterogeneity.
Vandenberghe ME et al. Sci Reports 2017.

12. Image Analysis Benefits
• Better accuracy (more precise quantitative measurements).
• Standardization (more reproducible results, especially for
intermediate categories & complex scoring systems).
• Efficiency (reduce time consumption for pathologists, especially for
performing mundane tasks like counting, & triage cases – such as
weed out negative cases).
• CAD will soon help pathologists find, diagnose & grade cancer.

14. Killer App in Digital Pathology
Does something the microscope can’t
accomplish

15. Immunoscore

16. Predictive Medicine (Imaging vs Omics)

17. - Developed C-Path (Computational Pathologist)
system to measure a rich quantitative feature set
from breast cancer epithelium and stroma (6642
features).
- Included both standard morphometric
descriptors of image objects and higher-level
contextual, relational, and global image features.
- Their findings implicated stromal morphologic
structure as a previously unrecognized
prognostic determinant for breast cancer.

18. Google Image Search Engine (GISE)
50%
83%

20. Developing Image Algorithms

21. Machine Learning Approaches
Image
Analysis
Approach
Traditional Algorithm Deep Learning
App design Expert annotation Training dataset
Application Calibration Re-Train
Drawback Human dependent Data size & quality
Regulations Expected Plausible

22. Classic Image Analysis Steps
1. Image pre-processing (e.g. color normalization)
2. Classification
3. Detection (identification)
4. Segmentation
5. Feature extraction
6. Quantification

23. PD-L1 (CD274)
Teixido et al. Cancer Biol Med. 2015; 12(3):259IC = immune cells

26. Image courtesy of Dr. H. Guo

27. Image courtesy of Dr. H. Guo

28. Variables
• Pre-analytical
– Tissue handling (collection, fixation, processing)
– Slide preparation (section thickness, artifacts like folds)
– Stain variation (IHC platform, color variation)
– Image acquisition (scanner difference, compression, etc.)
• Analytical
– Algorithms limited by file format & magnification
– Measurements vary with different algorithms
– Do you analyze regions of interest (ROI) vs. WSI
– Tumor heterogeneity (e.g. “hotspots”)
– Artifacts (tissue folds, air bubbles, crushed tissue, overlapping cells)
– Counting errors (e.g. cells between frames)
• Post-analytical
– e.g. Human interpretation, IT support

29. Image Compression (IHC view)

30. Image Compression (IA view)

31. Brightness Contrast
Compression Blurring

32. Her2/neu
Slide & Tissue ArtifactsSlide & Tissue Artifacts

33. Comparison of Algorithms
Combrinck M, Fine J, Pantanowitz L. J Pathol Inform 2015, 6:S3-S4.
ER Ki67

34. Within Scanner Variability

35. Automated prostate gland segmentation
Aperio Phillips Ventana

37. QIA Guideline from CAP
• Scope:
– Provide recommendations for improving reproducibility, precision,
& accuracy of QIA for HER2 by IHC
• Topics:
– Algorithm selection (e.g. locked down, FDA-approved only?)
– System validation (what is appropriate for clinical use?)
– Calibration (reproducibility of results and controls to be used?)
– Training & operation (which staff to involve, ROI selection?)
– Performance monitoring (QA and change control process?)
• Methods:
– Expert & advisory panels
– Systematic literature review
– Publication expected soon

39. Platforms

40. Hazards of AI & Data Mining
• Many failed projects
• Inaccurate predictions
• Inappropriate modeling
• Reliability of input data
• Technological mistrust
• Accountability

43. GIGO Principle

44. DATA MODEL RESULT

45. DATA MODEL RESULT

46. DATA MODEL RESULT

47. DATA MODEL RESULT

48. e.g. Criminal Machine Learning
• Wu & Zhang. Automated
inference on criminality using
face images. arXive. Nov 2016.
• ML to detect human face features
(1,800+ photos).
• Accurately (90%) distinguished
criminals vs. non-criminals.
• Only non-criminals were faintly
smiling!

49. Pathologists as Information Scientists
• Pathologists have always embraced technology in the lab.
• Some tasks once performed manually have been automated
(e.g. cell counts, Pap tests), leaving pathologists with more
complex tasks.
Jha & Topol. JAMA 2016; 316 (22)
• But can AI perform the more complex tasks of pathologists?
• And, in some instances, with superior accuracy?

50. Take Home Message
• Just because a computer gives us
an answer, it does not mean that it’s
always correct. Hence, pathologist
oversight is critical.
• “A fool with a tool is still a fool”.
Thus, safe use of CAD for routine
work requires calibration, validation
& practical guidelines.
• I think it’s unlikely that machine
vision will completely replace us.
Our jobs will not be lost; rather, our
roles will be redefined.

51. Questions?