1. New Machine Learning in Medical Imaging Journal Club
Adam Alessio, PhD, Radiology
David Haynor, PhD, MD, Radiology

2. Machine Learning in Medical Imaging
Goals:
Introductions
Brief Review of Terminology (review Erickson 2017 Radiographics )
Discuss Scope of Journal Club
Topical areas
Format of meetings
Initial list of possible journal articles
Potential other offerings if of interest

3. Introductions Name, Department,
Familiarity with Machine Learning Methods:
1 None
2 Some Familiarity (aware of basic concepts, no hands-on experience)
3 Familiar (some hands-on, taken intro class, read some lit)
4 Very Familiar (published papers on these topics, active ongoing projects)
5 Expert (developed new algorithms, wrote book chapter, numerous papers)

4. Labeled Data: Set of examples with correct answer
Erickson et al, Machine Learning for Medical Imaging. Radiographics, 2017
Machine Learning: Method to learn from a set of data, then apply methodology to make a prediction
Classification: Task of assigning a class or label to data (binary or more)
Training: Phase during which algorithm system has labeled data with answers
Labeled Data: Set of examples with correct answer
Training Set:
Validation Set:
Testing Set: For testing performance of a trained system
Node, Layer, Weights : Components of algorithm (usually nodes and layers predefined and training process finds optimal weights)

5. Supervised vs Unsupervised Methods:
Erickson et al, Machine Learning for Medical Imaging. Radiographics, 2017
Supervised vs Unsupervised Methods:
Supervised: learning from labeled/annotated data
Unsupervised: Learning from unlabeled data (no knowledge of groups present in training data)
Reinforcement Learning: Start with training data with labels. Then system improves with unlabeled data.
Feature Computation/Extraction – extraction of info to make decisions
Feature Selection – Selecting subset of features

6. Conventional Machine Learning:
Erickson et al, Machine Learning for Medical Imaging. Radiographics, 2017
Conventional Machine Learning:
Neural Nets
K-Nearest Neighbors
Support Vector Machines
Decision Trees
Naive Bayes (assumes features statistically independent)
Deep Learning:
multiple layers of nonlinear processing units (>20 layers) and
the supervised or unsupervised learning of feature representations in each layer
Examples: deep neural networks, convolutional neural networks, deep belief networks and recurrent neural networks

7. Other Items: Schedule?, Other Offerings?, Concerns?
Machine Learning in Medical Imaging
Suggested Scope For Journal Club Discussions:
Start out with deep neural networks and then move on to other applications and technologies later as time/interest warrant,
and our scope may include biomedical (non-radiology) imaging applications as well.
Other Items: Schedule?, Other Offerings?, Concerns?

8. Figueiredo. Nature biomed eng. 2017

9. Esteva Nature. 2017

10. Esteva Nature. 2017

15. Training Set Has 3500-5000 images

16. Gulshan JAMA. 2016

17. Gulshan JAMA. 2016