Comments (8)
Hi Bin,
Thank you very much! Please let me know when you check the magnifications you used. I think 5x and 20x would make more sense for the lung histology images.
Thank you very much for sharing the files. What magnification of the cropped patches have you uploaded to the Google Drive folder?
Many thanks, George
Those are exactly what I used for the experiments in the paper. I thought they were 20x / 5x but when I recently compared them to the view of 20x in slide viewer they seem to be 10x / 2.5x. (not 1.25x)
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Those are all for high-mag. I uploaded low-mag weights to the same folder.
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Thank you very much! What's the difference between v0 and v1 models?
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They are the same models trained for different time lengths using SimCLR (~3 days vs ~ 2 weeks), you could monitor the loss via tensorboard and stop the training once the loss stops to change much. The recommended batch size is at least 512, in order to obtain a good embedded. For TCGA you could also train the embedded without considering MIL first (use slide label for patch labels) and then use the embedded to compute features for MIL. I have also added an option for using ImageNet pre-trained CNN to compute features.
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Thank you very much for the explanation! Just to clarify, was v0 trained for 3 days, and v1 - for 2 weeks?
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Thank you very much for the explanation! Just to clarify, was v0 trained for 3 days, and v1 - for 2 weeks?
Hi George,
I remember v0 leads to worse accuracy, so v0 should be the one with the shorter training time. Plus I lately check that the patches for TCGA seemed to be cropped from 10x / 2.5x instead of 20x / 5x, so that was possibly a misstatement in the paper. I need to confirm this regarding the actual magnifications, Camelyon16 seems to be scanned with a different type of scanner while TCGA mostly consists of 40x Aperio, but some of them also seem to be other types of scanners with different magnification levels. I have uploaded the patches I cropped for the experiments just in case you need them: https://drive.google.com/file/d/17zCn-WRNzxxxh8kkdBTbDLDZy0XZ3RIu/view?usp=sharing . You could place these patches in the WSI folder and start with compute_feats
so on.
Best,
Bin
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Hi Bin,
Thank you very much! Please let me know when you check the magnifications you used. I think 5x and 20x would make more sense for the lung histology images.
Thank you very much for sharing the files. What magnification of the cropped patches have you uploaded to the Google Drive folder?
Many thanks,
George
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Thank you for both the clarification and for sharing the patches!
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Related Issues (20)
- Tree Features during testing
- About attention_map
- TCGA Labels HOT 1
- Reproducing the work on BACH (Breast Cancer Histopathology Dataset) from Kaggle.
- A question about the split of dataset HOT 2
- deepzoom_tiler freezes HOT 1
- the number of patches per bag
- Where is the abmil.py file? HOT 1
- Instance Prediction HOT 1
- How to get ACC of the Slide Class Prediction
- Inquiry Regarding FROC Score Calculation
- Wrong testing code or weights HOT 2
- Downloading TCGA Patches
- TCGA Dataset Training and Testing Distributions HOT 1
- about compute_feats.py and train_tcga.py Number of output classes question HOT 1
- About multiscale features and execution speed HOT 4
- Generate features for another dataset HOT 2
- Question about reproducing your article metrics
- Encountering problems while training models on the Camelyon16 dataset HOT 2
- Threshold for filtering background in different magnifications
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