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Medical Imaging (D3L3 2017 UPC Deep Learning for Computer Vision)

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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.

Published in: Data & Analytics

Medical Imaging (D3L3 2017 UPC Deep Learning for Computer Vision)

  1. 1. [course site] Verónica Vilaplana veronica.vilaplana@upc.edu Associate Professor Universitat Politecnica de Catalunya Technical University of Catalonia Medical imaging applications #DLUPC
  2. 2. Outline ● ● ● ○ ○ ○ ○ ○ ● 2
  3. 3. Why deep learning for medical imaging? 3
  4. 4. Why deep learning for medical imaging? 4 ● ● ●
  5. 5. 5 Why deep learning for medical imaging?
  6. 6. Why deep learning for medical imaging? 6
  7. 7. Deep learning uses in medical imaging 7
  8. 8. Challenges ● ● ● ● ● ● ● ● 8
  9. 9. Challenges ● ○ ○ ○ ● ● ● ○ ● ○ ○ 9
  10. 10. Challenges ● ○ ○ ● ● ○ ○ ○ ○ 10
  11. 11. Classification: Diabetic Retinopathy detection 11 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. 12. Classification: Diabetic Retinopathy detection 12 Szegedy et al, “Rethinking the Inception Architecture for Computer Vision”, Dec 2015
  13. 13. Classification: Diabetic Retinopathy detection 13
  14. 14. Classification: skin cancer detection 14
  15. 15. Classification: skin cancer detection 15
  16. 16. Classification: skin cancer detection 16
  17. 17. Classification: skin cancer detection 17 Inference classes Training classes
  18. 18. Classification: skin cancer detection 18
  19. 19. Classification: skin cancer detection 19 t-SNE visualization of the last hidden layer
  20. 20. Segmentation: cell segmentation in microscopy images ● ● 20Ronneberger et al, “U-Net: Convolutional Networks for Biomedical Image Segmentation”, arXiv 2015
  21. 21. U-Net ● ● ● 21
  22. 22. Segmentation and Classification with partial annotations 22
  23. 23. Segmentation and Classification with partial annotations 23
  24. 24. Segmentation and Classification with partial annotations 24* Simonyan et al, Deep inside convolutional networks: visualising image classification models and saliency maps, 2013
  25. 25. Segmentation: brain tumor segmentation 25
  26. 26. Segmentation: brain tumor segmentation 26
  27. 27. Segmentation: brain tumor segmentation 27 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. 28. Segmentation: brain tumor segmentation 28 3D-Net1 3D-Net3 3D-Net2
  29. 29. Segmentation: brain tumor segmentation 29 The importance of skip connections
  30. 30. Resources ● ○ ○ ○ ● ○ ○ ○ ● ○ ○ ○ 30
  31. 31. Questions? 31

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