Comments (4)
Hi @XinyangHan - sharing my answer publicly as well for others to see.
Regarding this point, we do not extract HIPT features with batch sizes greater than 1 (which you may have been doing). Rather, we patch + extract features from TCGA-3C-AALK-01Z-00-DX1.4E6EB156-BB19-410F-878F-FC0EA7BD0B53
with 4K patching yields ~117 4K patches in this slide and takes around 190 seconds (see the provided segmentation below).
One of the limitations of this work is that 4K patching for some slide can be difficult using the four_pt
contour function in CLAM, which was developed for four_pt
contour function to be less conservative and make sure that it at least includes 1 4K region per slide. For example, a biopsy slide (like the one shown below) may not be patched correctly and have any detected 4K patches using the current four_pt
contour function (there are many slides in TCGA that are just biopsy fragments and have very little tissue content). Using the exact splits that I evaluate is also OK, but if you are also evaluating on your own splits or custom dataset, I would try and relax the contour function.
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Thanks for your excellent work. Iām about to replicate your weakly-supervised experiment and assume I need to start with feature extraction using hipt4k pretrained model. In my initial tests, extracting ViT features seems lengthy, possibly taking over a week. Could you share how long this step took in your work, and the setup you used?
Hi! I am also reproducing this experiment. Would you want to discuss some results/progress?
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Yeah! Sure! Glad to chat!
from hipt.
Yeah! Sure! Glad to chat!
Cool! Do you have an email address maybe? (/ my email is written on my profile)
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Related Issues (20)
- .csv metadata file for colorectal dataset
- Got different results when runing the example "Using the HIPT_4K API" two times HOT 7
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