Comments (8)
@ilikewind yes, for Normal_*.json files they were generated by random sampling coordinates from the normal tissue mask at some higher level, e.g. level 6.
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@yil8 I think the Normal_*.json should include all normal tissue, e.g. Normal_001.json represent the tissue area of Normal_001.tif. so I was confused about random sampling...
thanks.
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@ilikewind It's always impossible to enumerate all the tissue regions of a normal samples, so you have to do subsampling.
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@yil8 In order to check what Normal_.json are, I translate Normal_001.json to Normal_001.xml, so I can visualiize it. but it is obvious that normal_.json include all normal tissue. It is a intersting to use normal*.json than normal tissue mask images.
and I also want to know more detail you employ for the normal_.json generated. thank you .
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@ilikewind I'm not quite sure how you generated this figure, it seems the blue dots are concentrated about boundary regions. This might due to the effect of hard negative mining.
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@yil8 the figure was generated by your file Normal-001.json at NCRF/jsons/train/.
And I am not sure if you mistake the json file for coordinates/normal_train.txt . It seems we are discuss different issues.
Best wish.
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@ilikewind I am so sorry, I was completely wrong about your question, and indeed I confused between Normal_001.json with coordinates/normal_train.txt. Normal_001.json was not really used through out this code base. Honestly, I forgot how Normal_*.json were generated, but they were probably not generated base on tissue mask
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@yil8 Ok, thanks for your patient. I will do something about it.
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Related Issues (20)
- What was the AUC and accuracy for the code? HOT 2
- 如何挑选较好的模型 HOT 1
- when I use probs_map.py, it will work,but when it will succeed,it report an error as following HOT 1
- NCRF 模型参数问题 HOT 18
- 难样本挖掘问题 HOT 1
- The ground truth of Test_026 too HOT 3
- 关于test数据集中mask.tif HOT 2
- Why is there no NCRF without hard negative mining in the probability map? HOT 1
- the coordinates of pre-sampled patches HOT 3
- request for ground truth mask of test_021.tif
- Cannot get correct FROC with resnet18 baseline ckpt HOT 6
- question about "probs * pairwise_potential - (1 - probs) * pairwise_potential" HOT 2
- Is this results achieved the SOTA before 2018? HOT 4
- What is "list.txt" HOT 5
- Have an error with data.cuda(async=True)
- A request for the ground truth of Test_026 too
- Can you explain how the code calculates the FROC score? HOT 2
- Efficient inference algorithm HOT 1
- I use matplotlib.pyplot.imshow to plot "Test_026.npy", but something go wrong, can u explain how to transpose "Test_026.npy"? HOT 2
- Project dependencies may have API risk issues
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