dazhangyu123 / acmil Goto Github PK

Hello! Thanks for your great work!
I run ACMIL on my own dataset, it gets good result and I want to draw an attention map to verify it, could you provide the code corresponding to attention map? Thanks.

Hi,
Thank you for your work. It's very amazing!
Is it possible for you to provide me with the Extracted patch features using SSL ViT-S/16 on camelyon16? I would appreciate it if you could provide it to me.
I am looking forward to your reply!

Thank you for your excellent work! You have provided the codes and operation modes of ABMIL, CLAM, DAMIL, TransMIL and DTFD-MIL, but I noticed that the comparison table of your paper also provided the comparison results of IBMIL and MHIM-MIL. Could you please provide the operation modes of IBMIL and MHIM-MIL?

Hi, it‘s a nice work, thanks for your contribution. But in table 1, I have some question about the performance of TransMIL and ABMIL. In your work, the results of ABMIL is better with both two backbones.

But in origional TransMIL paper, the results is the opposite one. So have you thought about the reason？Can this account for the different backbone？Since in TransMIL, they use res50 to extract features.

您好，贵项目的wsi_core目录下的WholeSlideImage.py中第18行的from utils.file_utils import load_pkl, save_pkl 运行出错，utils目录下没有file_utils文件，希望能得到您的反馈，谢谢

Thanks for your nice work. I would like to know in what way dino_vit_small_patch16_ep200.torch goes through the normalization of the training images because in practice we often put the test images through the same normalization process as the training set, e.g.

transforms_beit3 = transforms.Compose([
transforms.Resize((224,224), interpolation=3), transforms.
transforms.ToTensor(),
transforms.Normalize(mean= (0.5, 0.5, 0.5), std= (0.5, 0.5, 0.5))
])

trnsfrms_CTransPath_Brow_iBOTViT = transforms.Compose(
[
transforms.Resize((224,224)),
transforms.ToTensor(),
transforms.Normalize(mean = (0.485, 0.456, 0.406), std = (0.229, 0.224, 0.225))
]
)