tgcnn-act-graph 0.2

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Create Activation Graphs for TG-CNN Model

Produce graphs using the 3D CNN layers from the trained TG-CNN model. These graphs show which edges or timesteps are the most important during model prediction.

Activation mapping graphs for edges steps:

• Get the patient 'graph'
• Get the filter with the strongest differentiation of maximum activation
• Do element-wise multiplication between the filter and the patient graph
• Make the edges with zero activation grey
  • Where an edge is the x and y for each timestep.
  • A node is a new node even if the node has already been visited.
  • Nodes can be named e.g. r5_t1 (read code = 5, timestep = 1)
  • Use the element-wise ('activation') result to colour the edges red if the connection isn't zero.

Limitations:

• This method requires there to be no overlap in the CNN windows.

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The equations of how maximum activation is calculated is as follows:

To get the edge weights: For a given filter $f_{k}$, patient $p$, and time step $i$, the process can be summarised as:

1. Extract the slice:

   $ W_{i}^{(p, k)} = G_{p}[i:i+F, :, :], $

   where $F$ is the size of the filter in the time dimension, $G_{p}$ as the input tensor for patient $p$, $f_{k}$ as the $k$-th filter, and $W_{i}^{(p, k)}$ is the slice of the tensor $G_{p}$ at time step $i$ with the same dimensions as the filter $f_{k}$.

2. Apply the filter and sum the result:

   $ S_{i}^{(p, k)} = \sum (W_{i}^{(p, k)} \odot f_{k}), $

   where $\odot$ denotes element-wise multiplication.

3. Apply the leaky ReLU activation function:

   $ A_{i}^{(p, k)} = leaky ReLU(S_{i}^{(p, k)}, \alpha), $

   where $\alpha$ is the leaky ReLU parameter.

4. Compute the maximum activation value across all time steps:

   $ \text{max_activation_value}{p, k} = \max{i} (A_{i}^{(p, k)}) $

5. Combine these into one formula:

   $ Max Activation Value_{p, k} = \max_{i} \left( leaky ReLU \left( \sum (G_{p}[i:i+F, :, :] \odot f_{k}), \alpha \right) \right) $

USAGE

See examples in create_graphs.ipynb for how to use this code.

ROADMAP

See the Issues in GitHub for a list of proposed features and known issues.

TESTING

Run tests by using pytest test_graphs/test_calculations.py in the top directory.

LICENSE

Unless stated otherwise, the codebase is released under the BSD Licence. This covers both the codebase and any sample code in the documentation.

See LICENCE for more information.

ACKNOWLEDGEMENTS