The code covers the experiments in the paper i.e. the Thyroid nodules classification (DDTI dataset), and the Breast nodules classification on (BUSI dataset).
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The code is based on Pytorch 1.7.1. It uses a 3rd-party lib einops.
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To run the two paper experiments, just run the file Exp2-LTQ.py. The file defines the training and testing process. At the end of the file, the file calls loop(
$\cdot$ ) function twice, starting the two experiments.
Just run the Exp1-Baseline-models.py. The loop(torchvision.utils.
# code to run the experiment
loop(f'Exp2-PAPER-c-{ticks}', **{
'use_pretrained_ae' : None,
'input_channels' : ['image'],
'n_epoch1': 4000,
'n_epoch2' : 200,
'Yu' : True,
'data' : 'DDTI',
'n_embed' : 64,
'ae_plan' : [1, 32, 64, 128, 2048],
'cl_plan' : [64, 64, 64],
'centre_crop' : False,
})
Result:
precision recall f1-score support
0 0.9040 0.9286 0.9161 294
1 0.9874 0.9827 0.9851 1680
accuracy 0.9747 1974
macro avg 0.9457 0.9557 0.9506 1974
weighted avg 0.9750 0.9747 0.9748 1974
Confusion matrix:
[[ 273 21]
[ 29 1651]]
# code to run the experiment
loop(f'Exp2-PAPER-c-{ticks+1}', **{
'use_pretrained_ae' : None,
'input_channels' : ['crop', 'mask'],
'n_epoch1': 3000,
'n_epoch2' : 500,
'Yu' : True,
'data' : 'BUSI',
'n_embed' : 64,
'ae_plan' : [2, 32, 64, 1024],
'cl_plan' : [64, 64, 64, 64], # 0.9454
})
Result:
precision recall f1-score support
0 0.9476 0.9851 0.9660 1743
1 0.9710 0.9017 0.9351 966
accuracy 0.9553 2709
macro avg 0.9593 0.9434 0.9505 2709
weighted avg 0.9559 0.9553 0.9549 2709
Confusion matrix:
[[1717 26]
[ 95 871]]
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