The document discusses a senior scientist and professor's work on machine learning for medical imaging. It focuses on using deep learning approaches to analyze MRI breast exams, which can contain over 1500 images. The challenges include locating important diagnostic information across multiple image types and views. The scientist discusses developing automated systems using techniques like U-Nets and convolutional neural networks to segment breasts and detect lesions for improved cancer diagnosis and treatment planning. Accuracy improves with larger, higher quality training datasets. Interpretability is important for clinical acceptance of AI in medical imaging due to performance requirements.

1. Anne Martel, PhD
Senior Scientist, Physical Sciences
Sunnybrook Research Institute
Professor, Department of Medical Biophysics
University of Toronto
Vector Institute Faculty Affiliate
Machine Learning for Medical Imaging
Toronto Machine Learning Summit, 2

2. A Web-Based Image
Management Solution for
Advanced Workflows
Co-Founder, shareholder and CSO of Pathcore Inc, Toronto

3. There are more than 1500 images in a single MRI breast exam.
Where is the important information?

4. There are more than 1500 images in a single MRI breast exam.
Where is the important information?

5. There are more than 1500 images in a single MRI breast exam.
Where is the important information?

6. There are more than 1500 images in a single MRI breast exam.
Where is the important information?

7. There are more than 1500 images in a single MRI breast exam.
Where is the important information?

8. There are more than 1500 images in a single MRI breast exam.
Where is the important information?
What is the diagnosis?

9. There are more than 1500 images in a single MRI breast exam.
Where is the important information?
What is the diagnosis?
How should we treat?

10. Medical images can be multispectral and multidimensional
T2 weighted
T1 weighted
Diffusion weighted
T1w with contrast
T1w without contrast

11. Medical images can be multispectral and multidimensional
Sagittal view
Axial view

12. Training Data
3x3x5
Patches
Lesion detection with ANNs
Deep Artificial Neural Network Approach
to Automated Lesion Segmentation in
Breast DCE-MRI; 2015; H. Wu, A.Martel
45 input values 32 nodes 7 nodes
Pretraining with unlabeled cases
using a stacked denoising autoencoder
hidden layers
output layer
input layer

13. Breast MRI CAD pipeline
ANN Lesion Probability
Fully connected
neural network
Breast MRI screening

14. Breast MRI CAD pipeline
ANN Lesion Probability

15. Breast MRI CAD pipeline
RFC Lesion Detection
Lesions Non-Lesions
False positive
detection removal

16. Breast MRI CAD pipeline
RFC Lesion Detection
Lesions
Sagittal MIP
Lesion
Classification

17. Breast Segmentation
Breast MRI CAD pipeline
Lesion
Classification
Motion correction
RFC Lesion Detection
ANN Lesion Probability

18. MRI breast segmentation using a U-net
128x128 128x128
64 x 64 64 x 64
32 x 32 32 x 32
16 x 16
U-Net: Convolutional Networks for Biomedical Image Segmentation, Ronneberger et al,
MICCAI 2015

19. Effect of parameter tuning on segmentation accuracy
2D Unet 3D Unet
Homa Fashendi
Grey Kuling

20. MRI breast segmentation
U-Net Data Type DSC
2D WOFS 0.96 ± 0.03
FS 0.94 ± 0.07
MIXED 0.94 ± 0.09
MULTI 0.95 ± 0.04
3D
WOFS 0.95 ± 0.02
FS 0.95 ± 0.04
MIXED 0.94 ± 0.03
MULTI 0.96 ± 0.02
WOF
S
F
S
Homa Fashendi
Grey Kuling

21. Effect of training dataset size and quality on segmentation
accuracy
Homa Fashendi
Grey Kuling

22. SPIE-AAPM-NCI BreastPathQ: Cancer Cellularity Challenge
http://spiechallenges.cloudapp.net/competitions/14

23. Assessing tumour burden
C
Automatic cellularity assessment from post-treated
breast surgical specimens.
Peikari et al, 2017, Cytometry A, 91:1078-1087

24. Inception network
Image in
Prediction out
 Approx 1500 training patches
 10 million learned weights

25. Shazia Akbar

26. S.Akbar et al, “Automated and Manual
Quantification of Tumor Cellularity in Digital Slides
for Tumor Burden Assessment”, submitted to
Histopathology

27. In digital pathology, scale really matters!
Normal duct
Duct carcinoma in situ

28. Duct carcinoma in situ Normal duct
In digital pathology, scale really matters!

29. Why is AI in medical imaging challenging?
 Performance has to be excellent
 Interpretability is essential for clinical acceptance
 Need to understand how the images are acquired and what they
represent
 Labelled training data sets are scare and expensive

30. • Mohammad
• Peikari
• Shazia Akbar
Nikhil
Seth • Azadeh
• Yazanpanah
• Rushin Shojaii
Cristina Gallego-Ortiz
• Andrei
• Mouraviev
• Yingli Lu
• Sharmila
• Balasingham
Hongbo Wu
• Sylvester Chiang
Radiology
• Belinda Curpen
Radiation oncology
• Eileen Rackovitch, MD
Anatomic Pathology
• Sharon Nofech-Mozes, MD
• Sherine Salama, M