Deep learning image classificaiton informed by expert attention
Project description
Expert-attention guided deep learning for medical images
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Install requirements.txt
Download Pytorch matching with a CUDA version matching your GPU from here.
Run train.py
Use the trained model for inference
Check out example.py for a simple example of how to use the trained model for inference.
When forwarding image through the network, use the argument collapse_attention_matrix=True
to get the attention matrix
to get the attention matrix averaged across all heads and keys for each query token.
y_pred, attention_matrix = model(image_data, collapse_attention_matrix=False)
For example, if you have 32 * 32 patches,
the attention matrix will be of size (32 * 32 + 1) 1025. Plus one for the classificaiton token.
If you set collapse_attention_matrix=False
, the attention matrix will be
uncollapsed. The resulting attention matrix will be of shape (n_batch, n_heads, n_queries, n_keys). For example, if you have 32 * 32 patches,
one image and one head, the attention matrix will be of shape (1, 1, 1025, 1025).
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