The primary metric used to assess the performance of the algorithms in the Glioma-MDC 2025 challenge is the F1 Score. The F1 Score provides a balanced measure of the algorithm's detection accuracy by combining both precision (the proportion of correctly identified mitotic figures out of all detected figures) and recall (the ability to identify all true mitotic figures). It is particularly useful in scenarios where there is an imbalance between the number of true positives and false positives/negatives.

- Precision (Positive Predictive Value): The proportion of true positive mitosis detections among all detected mitoses. 

- Recall (Sensitivity): The proportion of true positive mitosis detections among all actual mitoses in the ground truth. 

- F1 Score: The harmonic mean of precision and recall, which provides a single measure of the detection accuracy. 

This metric will be the primary determinant in ranking the participating algorithms, reflecting their overall ability to accurately detect and classify mitotic figures within the provided glioma tissue images. Additionally, precision and recall will be analyzed separately to provide further insights into each algorithm's classification accuracy and detection sensitivity, respectively. 


## Submission File
We have provided a test outcome table `Submission_template.csv`, please fill in the column prediction with your predicted labels (Mitosis: 1; Non-mitosis: 0). 
(**Note**: Each image in the test set corresponds to a JSON file, and the JSON file contains cell coordinates marked with "Blank1", "Blank2" and so on.)

e.g.,
```
testing0001,Blank1,0
testing0001,Blank2,1
...
```