Comments (2)
Hello, I have encountered some confusion. There are two different patterns in the code for the CE indicator you provided. For example, res_mlc['F1_MACRO'] = f1_score(gt, pred, average="macro") res_mlc['F1_MICRO'] = f1_score(gt, pred, average="micro") which method is the most standard in your paper?
Hello! Have you got the MIMIC-cxr dataset? I'm confused about the size of dataset. I have got an account, but my net is not so stable that I dont't know whether the dataset is completely downloaded. Could you tell me the size of the every file? especially the named "files" in the dataset. Thank you very much!
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Hello, I have encountered some confusion. There are two different patterns in the code for the CE indicator you provided. For example, res_mlc['F1_MACRO'] = f1_score(gt, pred, average="macro") res_mlc['F1_MICRO'] = f1_score(gt, pred, average="micro") which method is the most standard in your paper?
Hello,
Where did you find the code regarding the calculations for the CE indicator? I can't seem to find it.
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Related Issues (20)
- 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
- Datasets HOT 2
- about MIMIC dataset
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