https://telecombcn-dl.github.io/2017-dlcv/ Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.

1. [course site]
Verónica Vilaplana
veronica.vilaplana@upc.edu
Associate Professor
Universitat Politecnica de Catalunya
Technical University of Catalonia
Medical imaging
applications
#DLUPC

2. Outline
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3. Why deep learning for medical imaging?
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4. Why deep learning for medical imaging?
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Why deep learning for medical imaging?

6. Why deep learning for medical imaging?
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7. Deep learning uses in medical imaging
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8. Challenges
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9. Challenges
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10. Challenges
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11. Classification: Diabetic Retinopathy detection
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Google: V. Gulshan et al, “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic
Retinopathy in Retinal Fundus Photographs”, Journal of American Medical Association, Dec. 2016

12. Classification: Diabetic Retinopathy detection
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Szegedy et al, “Rethinking the Inception
Architecture for Computer Vision”, Dec 2015

13. Classification: Diabetic Retinopathy detection
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14. Classification: skin cancer detection
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15. Classification: skin cancer detection
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16. Classification: skin cancer detection
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17. Classification: skin cancer detection
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Inference classes
Training classes

18. Classification: skin cancer detection
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19. Classification: skin cancer detection
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t-SNE visualization of the last hidden layer

20. Segmentation: cell segmentation in microscopy images
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20Ronneberger et al, “U-Net: Convolutional Networks for Biomedical Image Segmentation”, arXiv 2015

21. U-Net
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22. Segmentation and Classification with partial annotations
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23. Segmentation and Classification with partial annotations
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24. Segmentation and Classification with partial annotations
24* Simonyan et al, Deep inside convolutional networks: visualising image classification models and saliency maps, 2013

25. Segmentation: brain tumor segmentation
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26. Segmentation: brain tumor segmentation
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27. Segmentation: brain tumor segmentation
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Casamitjana et al, 3D Convolutional Networks for Brain Tumor Segmentation: a comparison of multiresolution architectures, 2016.
(1) Havaei et al, Brain tumor segmentation with Deep Neural Networks, 2016
(2) Kamnitsas, Efficient Multi Scale 3D CNN with fully connected CRF for Accurate Brain Lesion Segmentation, 2016

28. Segmentation: brain tumor segmentation
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3D-Net1 3D-Net3
3D-Net2

29. Segmentation: brain tumor segmentation
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The importance of skip connections

30. Resources
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31. Questions?
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