This talk describes the recent progress in how we process cancer screening and IHC quantification using machine learning techniques.

1. AI-Powered Medical Imaging
Analysis for Precision Medicine
Sean Yu, VP of Data & Machine Learning
2021/05/01

2. aetherAI
Asia’s Leading Medical Image AI Company
• Founded in Oct 2015
• Digital pathology and AI-Powered Medical Imaging
• Largest share of the digital pathology market in Taiwan
• 60+ Employees (MDs, Data scientists, Web engineers, Systems engineers, MBAs)
• 5+ Main products
• 10+ Ongoing medical image AI projects
• 7 Journal papers in the past two years

3. Research and Business Partnerships

4. aetherSlide
Human-centered & AI-ready Platform
for Pathologists
A powerful cloud-based platform
integrates pathologists’ knowledge
and advanced AI techniques to
achieve precision medicine

5. Cancer Identification
- AI as a screening tool

6. Extreme Resolution of Whole-Slide Images
ImageNet: 224 x 224 Chest X-ray: 2,000 x 2,000 400x WSI: 200,000 x 100,000

7. Extreme Resolution of Whole-Slide Images
ImageNet: 224 x 224 Chest X-ray: 2,000 x 2,000 400x WSI: 20,000 megapixel
iPhone 12 Pro Max
12 megapixel
Google Pixel 5
12.2 megapixel

8. Developing Cancer Classifier
- Common Workaround：Patch-based Method

9. ● Laborious human annotations
● Inconsistency between experts
Developing Cancer Classifier
- Common Workaround：Patch-based Method

10. ● Laborious human annotations
● Inconsistency between experts
Developing Cancer Classifier
- Common Workaround：Patch-based Method

11. ● Trapped in Local minimum
● Inefficient information utilization
● Atypical patterns?
Developing Cancer Classifier
- Advanced Workaround：Multiple Instance Learning
Campanella et al. (2019)

12. ● Let a CNN consumes whole-slide images DIRECTLY.
● Slide-level ground-truths can be automatically acquired from medical records,
scalable to all accessible data in medical centers.
(~1,000 slides/day v.s. 8 annotated-slides/day)
Developing Cancer Classifier
- Our Method：Whole-Slide with Huge Model Support

13. Nasopharyngeal Carcinoma Classification
Whole-Slide model VS. Patch-based model
Benign or NPC ?
Two-stage workflow
for cancer screening

14. Nasopharyngeal Carcinoma Classification
Whole-Slide model VS. Patch-based model
Patch-based model Whole-Slide model
Input Size Effect Training Size Effect
~ 1,000 slides

15. Conducting Whole-Slide Training
- Operation optimization for practical use
From 1 month to 3 days

16. Conducting Whole-Slide Training
- Operation optimization for practical use
From 1 month to 3 days

17. Lung Adenocarcinoma/Squamous-cell Carcinoma Classification
Whole-Slide model VS. MIL model
~ 10,000 slides

18. Application
- Cancer Screening on aetherSlide
• Fast navigation to hot-
spots
• Cancer purity of
– whole-slide images
– within selected region
Select Candidate region for NGS
Micro metastasis localization

19. IHC Module
- AI as a quantification tool
Use Case
• Ki67 / ER / PR
• PD-L1 / Her2
Special Use Case
• H3K27
• Ki67&CD68 double staining
Ki67 H3K27me3
PD-L1 Her2

20. on 3 high-power-fields (HPFs)? or whole-slide image?
sample 100 cells? 500 cells? or more cells?
IHC Response Estimation on Whole-slide Images
- Human evaluation can be subjective & discrete

21. IHC Response Estimation on Whole-slide Images
- Human evaluation can be subjective & discrete
Allred scoring system for ER/PR
Ki-67, WHO (2017)
Credit: NigerJSurg (2018)

22. IHC Response Estimation on Whole-slide Images
- Human evaluation can be subjective & discrete
Agilent Dako, PD-L1 IHC 22C3 Scoring
Guideline
Roche PD-L1, SP142

23. IHC Response Estimation on Whole-slide Images

24. IHC Response Estimation on Whole-slide Images
Positive Rate ~ 53%

25. Automated Ki67 quantification
- Performance comparison
10-20 min
?

26. Special case of automated Ki67 quantification
- Ki67/CD68 double staining
Ki67- / CD68+
Ki67+ / CD68+
Ki67+ / CD68-
Average correlation:
0.882

27. Special case of automated nuclei quantification
- H3K27me3 response evaluation

28. Automated PD-L1 quantification

29. Automated Her2 quantification

30. • Can consuming more data
• Can have objective criteria, which is consistent
across
• People
• Time
• Space
• Can identify more features/parameters correlated
to
• Clinical indexes
• Prognosis
• Treatment response
In the future …
With AI-powered Medical Image Analysis System
NTUH: 1,250 slides / day
CGMH: 3,800 slides / day

31. • Can consuming more data
• Can have objective criteria, which is consistent
across
• People
• Time
• Space
• Can identify more features/parameters correlated
to
• Clinical indexes
• Prognosis
• Treatment response
In the future …
With AI-powered Medical Image Analysis System
Karimi et al. (2019)
Mercan et al. (2020)

32. • Can consuming more data
• Can have objective criteria, which is consistent
across
• People
• Time
• Space
• Can identify more features/parameters correlated
to
• Clinical indexes
• Prognosis
• Treatment response
In the future …
With AI-powered Medical Image Analysis System
Ole-Johan Skrede et. al.
(2020)
Echle et al. (2021)

33. From precision diagnosis to precision medicine

34. Prevision the Future of AI