Identification of Kernels in a Convolutional Neural Networkjaa5/files/Presentations/RDSC19.pdf ·...
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Identification of Kernels in a Convolutional Neural Network Jonas Actor Rice University 14 October 2019 [email protected]
Transcript of Identification of Kernels in a Convolutional Neural Networkjaa5/files/Presentations/RDSC19.pdf ·...
Identification of Kernels in a Convolutional NeuralNetwork
Jonas ActorRice University
14 October 2019
Target application: medical image segmentation
Abdominal CT scan Liver and tumor segmentation
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Classical Approach: Level Set Equation
Level Set Equation: time = 200 2
Classical Approach: Level Set Equation
Level Set Equation: time = 200 2
Classical Approach: Level Set Equation
Level Set Equation: time = 500 2
Classical Approach: Level Set Equation
Level Set Equation: time = 500 2
Classical Approach: Level Set Equation
Level Set Equation: time = 1100 2
Classical Approach: Level Set Equation
Level Set Equation: time = 1100 2
Classical Approach: Level Set Equation
Level Set Equation: time = 1500 2
Classical Approach: Level Set Equation
• Well-established theory to analyze approximation, stability• Upwind finite differences + fast marching method• Semiautomated : requires initialization by user• Works for simple problems only : relies on edge information
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Motivation: LSE vs CNN
Can analyze? Accurate?LSE yes sometimesCNN no yes
Goal: accurate method we can analyze
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Similarities between LSE and CNN
LSE CNN
Convolution finite difference kernel learned kernel
1h2
0 1 01 −4 10 1 0
k11 k12 k13k21 k22 k23k31 k32 k33
ReLU upwind scheme activation function
max(0,D+ ∗ u) + min(0,D− ∗ u) max(0,K ∗ x + b)
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Build a CNN like a LSE solver
Level Set Equation −→ Level Set Network
1 Discretize Level Set Equation• Explicit forward Euler in time• Upwind finite differences in space
2 Forward Euler −→ residual skip connections3 Upwind finite differences −→ convolutions and ReLU
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LSN: Results
K-Fold LSE LSN Test LSN Validation UNet0 0.736 0.837 0.619 0.9121 0.600 0.847 0.729 0.9192 0.483 0.116 0.005 0.8743 0.730 0.827 0.606 0.8954 0.643 0.831 0.596 0.915
Avg 0.604 0.692 0.511 0.903Avg−{2} 0.640 0.837 0.638 0.911
Table: DSC scores for each fold, from training the level set network.
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Identification of Kernels
• Are CNN convolution kernels finite difference stencils?• Are they close to finite difference stencils?• What about other standard image processing kernels?
Numerical analysis kernels• Laplacian• Edge detection• Identity
Image processing kernels• Gaussian blur• Local mean• Sharpen
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Setup of CNN
1 2
3 4
5 6
7 8
10 11 129
13 14
15 16
17 18
= Dropout + Conv2d + ReLU
#
Dense
IN OUT
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1
16 16
16 16
16 32 32
32 64 64 32 32
32
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16
16
1
16
1616
16 16
16
32
32
32
32
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16
MaxPool2dUpsample2d
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16Concatenate
16 # Channels in/out
Trained on MICCAI LiTS 2017 dataset for liver segmentation
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Kernel Analysis
• For each layer, separate each channel’s 3× 3 convolution kernel• Flatten each 3× 3 kernel into a vector ∈ R9
• Cluster with k-means• Project down using PCA• Project known numerical analysis and image processing kernels
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Kernel Clustering Results
Figure: Convolution Layer 11 (encoder) 11
Kernel Clustering Results
Figure: Convolution Layer 14 (decoder) 12
Conclusions
• Level Set Equation 6= Level Set Network 6= UNet• Framework for using same operations (convolutions + ReLU) for
both NNs and PDEs• Examine how learned CNN kernels change across different layers
JAA is supported by the NLM Training Program in BiomedicalInformatics & Data Science (T15LM007093), supplemented by theKen Kennedy Institute Computer Science & Engineering EnhancementFellowship, funded by the Rice Oil & Gas HPC Conference. 13
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