Comments (2)
Hi,
- They were not filtered. We haven't investigated how this affect the results but I doubt it would affect much the result given the low proportion of tiles maked.
- You can have a look at our paper on Lung cancer. with cohort were the tumor content is low, this is true. But for the TCGA dataset, the tumor content is really high so the proportion of falsely labelled tiles is really low. Unless you train for too long and over-fit, AUC is good. It could probably have been even better if we had carefully manually selected regions with high tumor content
- Yes. Per tile then aggregated per slide.
- No, only the magnification (which is slightly equivalent)
- We have another paper coming in biorXiv which you can check
Best,
N.
from deeppath.
Hi,
- They were not filtered. We haven't investigated how this affect the results but I doubt it would affect much the result given the low proportion of tiles maked.
- You can have a look at our paper on Lung cancer. with cohort were the tumor content is low, this is true. But for the TCGA dataset, the tumor content is really high so the proportion of falsely labelled tiles is really low. Unless you train for too long and over-fit, AUC is good. It could probably have been even better if we had carefully manually selected regions with high tumor content
- Yes. Per tile then aggregated per slide.
- No, only the magnification (which is slightly equivalent)
- We have another paper coming in biorXiv which you can check
Best,
N.
Hi Nicholas, I am really appreciated for all your kindly help!
All the best,
D
from deeppath.
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