Comments (2)
Dear @ashwanikumar04,
Thanks for your attention to our paper!
We directly used the CheXpert labeler (https://github.com/MIT-LCP/mimic-cxr/
tree/master/txt/chexpert) to label the generated and ground-truth reports and then compute the precision, recall, and F1.
Best,
Zhihong
from r2gen.
Hello:
Thanks for sharing the code.
While going through the code, I did not find the code that calculates the clinal accuracy (Precision, Recall, and F1) metrics calculation. Will it be possible to share that?
Thanks
Hello, did you know how to get the code for clinical accuracy (Precision, Recall, and F1) , I used the Chexpert to get the label, but I dont know how to calculate the accuracy.
If you do know, could you plz share the code?
Thanks.
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Related Issues (20)
- The result of each run
- Code to visualize the attention map
- About CE Metrics
- How to calculate overall Precision, Recall and F1 with these values of 14 categories? HOT 4
- dataset issues HOT 2
- 关于MIMIC数据集的拆分问题
- The definition of ground truth report
- Wrong number of patients on the official IU_XRAY instances in drive data link. HOT 2
- Caption Generation HOT 30
- Access to MIMIC-CXR HOT 1
- Does difference between torch and torchvision versions cause a big difference in evaluation results?
- Base Model without RM and MCLN
- self.examples[i]['mask'] = [1] * len(self.examples[i]['ids'])
- MIMIC Dataset
- Do not have datasets access HOT 3
- Problem on installing requirements
- Can't Download MIMIC-CXR Dataset
- A beam search error occurs during the referencing process
- About Mimic Dataset
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