U-Net (1).pptx

U-Net

1. U-Net: Convolutional Networks for Biomedical Image Segmentation 2022/04/26 Changjin Lee

2. Semantic Segmentation - Assign each pixel a class label

3. FCN Gradually generate higher-level feature maps as the network gets deeper Transposed Convolution to generate segmentation map Model Variations - Utilize “skip-connection” by summation higher-level (context information) low -level (ex. edges, curves)

4. U-Net: Idea The final feature map from the series of convolutional layers contain high-level and contextual information We cannot just jump from this “ higher-level ” feature maps to segmentation map which requires “ low-level ” and dense predictions To remedy this issue, FCN introduces “skip-connection” which uses “ summation ” U-Net introduces a better structured network architecture to narrow down this disparity U-shape architecture Skip connection by concatenation

5. U-Net Contracting path channels increase resolution decreases gradually generate higher-level features Expansive path transpose convolution channels decrease resolution increase more precise localization Skip-connection by concatenation Unlike FCN, U-Net “ concatenates ” the feature maps from the contracting path with the expansive path contracting path expansive path concatenation

6. U-Net higher level lower level d1 d2 d3 d4 u 1 u2 u3 u4 Propagate contextual information to the similar abstraction level lower level higher level

7. Contracting Path 1/2 resolution, 2 channels Double Conv 3 x 3 filter no padding - resolution decreases after each conv layer But in practice, often use padding to match dimension ReLU 2 x 2 Max Pooling, stride 2

8. Bottleneck Layer

9. Expansive Path 2 resolution, 1/2 channels 2 x 2 T ranspose Convolution Skip Connection - Concatenation Feature map from the contracting path is cropped to match the dimension for concatenation Double Conv ReLU

10. Final Layer Now we have to assign a class label to each pixel value 1 x1 conv layer ( n filters ) where n is the number of classes Final Output: (B, n, H, W) , n = # classes Softmax to produce segmentation map

11. Overlap-tile Strategy Image with any arbitrary size can be used as input To efficiently use GPU, an input image divided into “tiles” and each tile is fed into the network Since we use zero padding , the final output and the input have different resolution In practice, often we use padding to produce equal-resolution output Hence, extrapolate the border by mirroring the input image

12. Pre-computed Weight Map d1 distance to the border of the closest cell d2 : distance to the border of the second closest cell

13. Data Augmentation The paper was originally designed for microscopic medical segmentations hard to obtain large training dataset -> heavy augmentation is necessary shift and rotation elastic deformations gray value variations smooth deformations drop-out layers

14. Experiments

15. References [1] https://arxiv.org/pdf/1411.4038.pdf [2] https://arxiv.org/abs/1505.04597