Deep learning is a type of machine learning that uses multiple processing layers to learn representations of data with features that become more complex at each layer. Deep learning has achieved human-level performance in areas like image recognition by learning from large datasets. In healthcare, deep learning has been applied to tasks like detecting pneumonia from chest X-rays and skin cancer from images with accuracy comparable to doctors. However, challenges remain around data variability, uncertainty, class imbalance, and data annotation. Cross-area collaboration and data sharing are seen as key to realizing the potential of deep learning in healthcare.

1. Deep Learning
+ Healthcare
Thomas Paula
May 24, 2018 - HCPA
=

2. Thomas Paula
● Machine Learning Engineer and Researcher @HP
● Msc in Computer Science
● POA Machine Learning Meetup
● @tsp_thomas
● tsp.thomas@gmail.com
Who am I?
2Deep Learning and Healthcare

3. Why should you bother
about this Deep
Learning thing?
3

4. 300+
4
papers regarding deep
learning and medical
image analysis
Source: Litjens, Geert, et al. "A survey on deep learning in medical image analysis." Medical image analysis 42
(2017): 60-88.

5. 5
Hinton said: "(...) people should stop training
radiologists now, it's just completely obvious that
in five years deep learning is going to do better
than radiologists, it might be ten years".

6. Agenda
● Introduction
● What is Deep Learning?
● Why now?
● Deep Learning applied to healthcare
● Challenges
● Conclusion
6Deep Learning and Healthcare

7. Introduction
7Deep Learning and Healthcare

8. What is Deep Learning?
Multiple definitions, however, these definitions have in common:
● Multiple layers of processing units;
● Supervised or unsupervised learning of feature representations in each layer, with
the layers forming a hierarchy from low-level to high-level features.
8Deep Learning and Healthcare

9. Data is Compositional
Sources: Convolutional Deep Belief Networks. Honglak Lee, et. al. and Large
Scale Deep Learning. Jeff Dean, joint work with Google.
9Deep Learning and Healthcare

10. Traditional Approaches
Input Feature Extraction Classification
● Expert knowledge
● Time-consuming hand-tuning
● In industrial applications, it is 90% of the time
● Domain-specific
10

11. “Intuition” of what features are
11

12. Traditional vs Deep Learning
Traditional
DeepLearning
12

13. Neuroscience: inspiration
13Deep Learning and Healthcare
Source: https://www.youtube.com/watch?v=QzkMo45pcUo&feature=youtu.be&t=6m7s

14. Neuroscience: inspiration
● Hubel and Wiesel Nobel Prize in 1981
14Deep Learning and Healthcare

15. First CNN application: digits recognition
15
1998
Source: LeCun, Yann, et al. "Gradient-based learning applied to document recognition." Proceedings of the IEEE 86.11 (1998).

16. Deep Learning is not
new… so what happened?
16

17. 17
Data Hardware Algorithms

18. Deep Learning examples
18

19. Images
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Image Translation

20. Audio
20Deep Learning and Healthcare
Speech Synthesis (Apple) Music Recommendation (Spotify)

21. Text
21Deep Learning and Healthcare
Text Summarization

22. Games
22

23. Deep Learning and Healthcare
examples
23

24. Pneumonia Detection on Chest X-Rays with Deep Learning
24Deep Learning and Healthcare
2017
Source: Rajpurkar, Pranav, et al. "CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning." (2017).

25. Classification of Skin Cancer with Deep Neural Networks
25Deep Learning and Healthcare
Source: Esteva, Andre, et al. "Dermatologist-level classification of skin cancer with deep neural networks." Nature (2017)
2017

26. Arrhythmia Detection
26Deep Learning and Healthcare
2017
Source: Rajpurkar, Pranav, et al. "Cardiologist-level arrhythmia detection with convolutional neural networks." (2017).

27. An Augmented Reality Microscope for Cancer Detection
27Deep Learning and Healthcare
Source: An Augmented Reality Microscope, Google Research Blog, 2018.

28. An Augmented Reality Microscope for Cancer Detection
28Deep Learning and Healthcare
Source: An Augmented Reality Microscope, Google Research Blog, 2018.

29. An Augmented Reality Microscope for Cancer Detection
29Deep Learning and Healthcare
Source: An Augmented Reality Microscope, Google Research Blog, 2018.

30. What are the challenges
and opportunities?
30

31. Challenges/Opportunities
● ML to be used as a tool (e.g. like stats)
● Unbalanced classes (e.g. more non-cancer than cancer examples)
● Data creation and annotation
○ Cumbersome process
○ Experts don’t always agree
● Variability of the data (e.g. MRI)
● Uncertainty
● How to leverage data from different sources (e.g. images and general
patient information)?
31Deep Learning and Healthcare

32. Take home message #1
Machine learning / deep
learning is here to stay.
32

33. Take home message #1
Machine learning / deep
learning is here to stay.
33
However, be aware of the hype!

34. Take home message #2
Cross-area collaboration
is essential.
34

35. Take home message #3
Data creation and sharing is a
cornerstone for the success of AI
in healthcare.
35

36. Questions?
Thank you!
Thomas Paula
May 24, 2018 - HCPA