Semantic Segmentation — U-Net

Fig 4. Semantic Segmentation
Fig 5. Instance Segmentation

Case Study: Data Science Bowl 2018

Fig 6. Masked Nuclei (left) and Original Image (right)
Fig 7. Vanilla U-Net
Fig 7. Vanilla U-Net Tensor Annotation
U-Net sample down block
U-Net sample up block
>>> input = torch.arange(1, 5).view(1, 1, 2, 2)
>>> input

(0 ,0 ,.,.) =
1 2
3 4
[torch.FloatTensor of size (1,1,2,2)]

>>> m = nn.Upsample(scale_factor=2, mode='nearest')
>>> m(input)

(0 ,0 ,.,.) =
1 1 2 2
1 1 2 2
3 3 4 4
3 3 4 4
[torch.FloatTensor of size (1,1,4,4)]
>>> input = torch.arange(1, 5).view(1, 1, 2, 2)
>>> input

(0 ,0 ,.,.) =
1 2
3 4
[torch.FloatTensor of size (1,1,2,2)]
>>> m = nn.Upsample(scale_factor=2, mode='bilinear')
>>> m(input)

(0 ,0 ,.,.) =
1.0000 1.2500 1.7500 2.0000
1.5000 1.7500 2.2500 2.5000
2.5000 2.7500 3.2500 3.5000
3.0000 3.2500 3.7500 4.0000
[torch.FloatTensor of size (1,1,4,4)]
Fig 8. Examples for transposed convolution operation with different parameters. Credit goes to https://github.com/vdumoulin/conv_arithmetic [12]
Fig 9. Overlapping nuclei mask
Fig 10. Weight map
Fig 11. (a) Raw image (b) ground truth different color for each instance (c) generated segmentation mask (d) pixel-wise weight map
Fig 12. For visual purposes foreground (green) contour (yellow) background (dark) classes

Transfer Learning and Beyond

TernausNet by Vladimir Iglovikov and Alexey Shvets

Fast.ai: Dynamic U-Net

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Machine Learning Engineer

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Kerem Turgutlu

Kerem Turgutlu

Machine Learning Engineer

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