Comments (2)
Hi,
Great work on the development of DIMR and DeepSNiF for noise removal on IMC data. In the article, it says that the final layer of the U-Net uses softplus as it's activation function (log(1+exp(x))), which I believe this means that the denoised images will be log-transformed. On this basis, do I have to perform normalisation (percentile-method) on the pixel values prior inputting into DIMR and DeepSNiF? Or can I input raw images for DIMR and DeepSNiF, and from this, is it still suitable to perform log normalisation on the single cell data after segmentation?
These ideas might not make sense at all, as I am still relatively new to IMC data processing. Nevertheless, thanks for your help!
Hello,
Thanks for your kind words on our software package. The softplus activation function does not change the scale of the data. Please check the plot of this function online. In this case, the output will not be log-transformed. Therefore, after denoising, just apply the data to other developed IMC pipeline.
Regards.
from imc_denoise.
Hi @PENGLU-WashU,
Thanks for the clarification. I will close this issue as this has been solved. Thank you!
from imc_denoise.
Related Issues (19)
- Image format HOT 5
- Val_loss HOT 4
- Updating tensorflow, cudnn and cudatoolkit HOT 1
- IMC_Denoise on M1 Mac HOT 3
- Logics behind data preprocessing HOT 2
- Isotype not found HOT 5
- Demo training data generates NaN for loss HOT 21
- Demo data produces NaN loss on multiple systems HOT 5
- IMC Denoising is too aggresive for certain channels HOT 9
- Issue generated patches from MIBI-TOF tiff data HOT 4
- multi markers training HOT 2
- About the issue of GPU usage efficiency HOT 3
- Percentage of masked pixels HOT 4
- running DIMR and DeepSNiF together in the tutorial HOT 1
- Question about how to train DeepSNiF properly & integration with steinbock HOT 24
- Problems running on GPU (NVIDIA A40) HOT 7
- Edge case in training batch generation
- N2V2
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