第１回NIPS2018（世界初！！）理論と実装を話し合う会の大政の資料

1. NIPS 2018
2018/10/29

2. ◾ h :
◾ a A6 6
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◾ I 4 4 e 6
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f b
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3. probabilistic U-Net
S. Kohl, et. al. “A Probabilistic U-Net for Segmentation of Ambiguous
Images ”
arXiv
https://arxiv.org/abs/1806.05034
my Qiita
https://qiita.com/masataka46/items/61615e069c03049aecff
GitHub tensorflow
https://github.com/SimonKohl/probabilistic_unet
my GitHub tensorflow
https://github.com/masataka46/probabilistic_UNet

4. probabilistic U-Net
Simon A. A. Kohl DeepMind
segmentation

5. 1.
2. VAE U-Net
3.

6. probabilistic U-Net
l CT
l
l

7. probabilistic U-Net
l Unet VAE
U-Net
VAE variational
auto-encoder
→

8. probabilistic U-Net
• U-Net
• X
S

9. probabilistic U-Net
X N
μ σ

10. probabilistic U-Net
Zi
U-Net

11. probabilistic U-Net
Zi
→

12. probabilistic U-Net
X ground truth
Posterior Net
N
μ σ

13. probabilistic U-Net
z U-Net

14. probabilistic U-Net
X S

15. probabilistic U-Net
Prior Net Posterior Net
KL-divergence

16. probabilistic U-Net
KL-divergence

17. probabilistic U-Net
Y, Y’
S, S’
tp
tn
fnfp
X Y
![#(%, ')]l x y

18. probabilistic U-Net
dropout
Dropout U-Net U-Net Ensemble M-Heads Image2Image VAE

19. Lung abnormalities segmentation
CityScapes dataset
•
• 4
•
•

20. a b

21. •
•

22. • U-Net VAE
• tf.linalg

23. my
• CityScapes dataset
• X
Ground Truth
output
input
z1 z2 z3 z4 z5 z6

24. l Probabilistic U-Net
l U-Net VAE