srinidhiPY
commented
2 years ago I am really sorry for the late reply.
In our experiments for BreastPathQ, we have used 69 WSI’s of the training set for pretraining the self-supervised model. I think the issue in your case may be because of 4 WSIs, which were not used.
As the number of patches that can be extracted from a given WSI mainly depends on the tissue content, there might be a high probability that you have lost some significant amount of data for SSL training.
On Sun, 25 Sept 2022 at 10:34, xiaozhu0816 @.***> wrote:
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For BreastPathQ, when I used the pretrian model author released and did the finetune, I could get a similar result to your paper.
But I tried to pretrain the model myself, following the instructions (1. Self-supervised pretext task: Resolution sequence prediction (RSP) in WSIs). I used the best pretrain model and did the same things as before. After finetuning, the result was not good enough like paper.
paper:[image: image] https://user-images.githubusercontent.com/72501744/192129127-48496486-edf8-4a6e-a180-975e4aea5e1e.png
my reslut: [image: image] https://user-images.githubusercontent.com/72501744/192129132-9028df2d-758f-42dd-855a-180e17b24c02.png
During my training, 4 WSIs which are bad wsis cannot be used. But I don’t think it is an essential problem for me, because just lose hundreds of data.
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For BreastPathQ, when I used the pretrian model author released and did the finetune, I could get a similar result to your paper.
But I tried to pretrain the model myself, following the instructions (1. Self-supervised pretext task: Resolution sequence prediction (RSP) in WSIs). I used the best pretrain model and did the same things as before. After finetuning, the result was not good enough like paper.
paper:
my reslut:
During my training, 4 WSIs which are bad wsis cannot be used. But I don’t think it is an essential problem for me, because just lose hundreds of data.