L'intelligence artificielle en radiologie, Pr Xavier Montet, 15.01.19

1. L’intelligence artificielle en radiologie: bénéfices et risques
2. Headlines
3. Scientific litterature
4. Artificial Intelligence (A.I.)
5. Artificial Intelligence (A.I.)
6. Neural Network
7. Neural Network
8. Humain vs Artificial Brain
9. Differentiating liver cyst from hemangiomas
10. Liver cyst and hemangioma
11. Unlabeled / unstructured data
12. Volume of data
13. Transfert learning: AlexNet
14. Down sampling the resolution
15. Down sampling the resolution
16. CNN training
17. Saliency map
18. Lung nodules detection
19. Lung nodules detection
20. Public data set from the NIH 108’948 Chest X-ray
21. Diagnostic accuracy
22. Images from NIH
23. After re-labelling
24. Fooled CNN
25. Generative adversarial network (GAN)
26. Generative adversarial network (GAN)
27. GAN in Radiology
28. Artificial Intelligence (A.I.)
29. Outline
30. Lung nodules detection
31. Lung nodules characterization ?
32. Steganography
33. Steganography
34. From Imaging to Radiomics
35. Numerical representation
36. Gray Level Co-Occurrence Matrix (GLCM)
37. Gray Level Co-Occurrence Matrix (GLCM)
38. Gray Level Co-Occurrence Matrix (GLCM)
39. Gray Level Co-Occurrence Matrix (GLCM)
40. Gray Level Co-Occurrence Matrix (GLCM)
41. Gray Level Co-Occurrence Matrix (GLCM)
42. Gray Level Co-Occurrence Matrix (GLCM)
43. Gray Level Co-Occurrence Matrix (GLCM)
44. Gray Level Co-Occurrence Matrix (GLCM)
45. Gray Level Co-Occurrence Matrix (GLCM)
46. Gray Level Co-Occurrence Matrix (GLCM)
47. Gray Level Co-Occurrence Matrix (GLCM)
48. Gray Level Co-Occurrence Matrix (GLCM)
49. Gray Level Co-Occurrence Matrix (GLCM)
50. Gray Level Co-Occurrence Matrix (GLCM)
51. Gray Level Co-Occurrence Matrix (GLCM)
52. Gray Level Co-Occurrence Matrix (GLCM)
53. Other Matrices
54. Features extraction from GLCM
55. Features extraction from GLRLM
56. Wavelets
57. Features extraction
58. Classification
59. lung nodules
60. Testing set
61. Radiomics
62. A guide to keep learning in healthcare
63. Take home message
64. Acknowledgement
65. Thank you

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