Deep learning image classificaiton informed by expert attention
Project description
Expert-attention guided deep learning for medical images
Get Started
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).
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for expert_informed_dl-0.0.2-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 4e420559df0108287c43d6eb364abf36a64223c4788ab80bb71a243abe58f7e9 |
|
MD5 | bd2fb8e532f01910e305db6207f7b21c |
|
BLAKE2b-256 | 862f59593579171f53c5c1fd439825ad7dcc7dead7708684ca1ba2d9e0fd8dbb |