Comments (2)
Hi, thanks for the interest in our work. Since this is segmentation, the "CAM" can be thought of as the feature map activations at any layer. Since in gradCAM and CAM, they take the last layer before things like batchnorm or dropout, I would suggest taking q_attn_conv
as the CAM layer. But you could also take the output of something like attn1
in the model and pool those features in the feature dimension. For example, the output of attn1 would be something like 128 * height * width
. You could average out the 128 to get a map that is just height * width
and that could be your CAM. Hope this helps! Oktay et al is the paper that originally proposed these attention Gates and they might have a way to generate them in their code repo.
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Hi, I was so happy and surprised to receive your reply so soon. Thank you so much for your detailed explaination to my problem, and your suggestion is also useful for me and other learners with similar question. Thus, i also suggest other learners to read the paper 'A NOVEL FOCAL TVERSKY LOSS FUNCTION WITH IMPROVED ATTENTION U-NET FOR LESION SEGMENTATION'.
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Related Issues (20)
- results HOT 7
- Version issue HOT 8
- gt_train issue HOT 1
- need guidance
- ValueError: continuous format is not supported HOT 1
- About the dataset HOT 1
- Multi class support HOT 2
- There is a problem to be solved HOT 3
- About ISIC dataset's folders.
- What does 'thresh' stand for? HOT 1
- Batch_Size HOT 1
- Learning Rate Decay or Typo?
- pred1 output nan after a few epochs
- Unet gating signal typo?
- val_dsc is bigger than 1? HOT 1
- loss: Nan. HOT 2
- Loss calculated on wrong dimension
- Gating Signal before Convolution
- multi-scale input in the attn_reg function
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