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binli123 / dsmil-wsi

DSMIL: Dual-stream multiple instance learning networks for tumor detection in Whole Slide Image
MIT License
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Patch-based method #28

Open qinghezeng opened 2 years ago

qinghezeng commented 2 years ago

Hi, thank you for this fantastic work!

I tested a similar patch-based method proposed here and also a attention-based MIL on TCGA LIHC dataset. It seems that the attention-based MIL outperformed the patch-based method in AUROC by 10%. Thus I am a bit supervised by the TCGA results (patch-based) shown in the Table 3 of your paper. I guess it is because of my sub-optical settings. I would appreciate if you could share your detailed settings used for the patch-based method (epochs, batch size, optimizer, loss, etc).

Thank you very much!

binli123 commented 2 years ago

The performance of patch-based training (without considering MIL) majorly depends on your dataset. If your positive bags are highly unbalanced, i.e., there are a lot of negative patches in a positive slide, then the patch-based training will perform very badly, such as the experiment for Camelyon16 shown in the paper.