Calculating patch-level metrics such as FROC about bayes-mil HOT 2 CLOSED

Hi,

Thanks for paying attention to our work.

Simply speaking, we treat the patch-level localization problem as a binary classification problem and compare the prediction labels with the patch-level groundtruths. In our paper, the groundtruths are provided by the patch-level labels in CAMELYON16 and CAMELYON17. The results are averaged over all patches in a slide and all slides in a dataset.

Notably, the calculation of patch level FROC follows <Localization Results, Section 4.1 Results on Camelyon16> in DSMIL. Specifically, "The reported FROC score is defined as the average sensitivity at 6 predefined false positive rates: 1/4, 1/2, 1, 2, 4, and 8 FPs per WSI."

Precision is computed by definition
average_over_all_slides(true_positive_patches_in_a_slide / ( true_positive_patches_in_a_slide + false_positive_patches_in_a_slide)).

Hope the information can help you. We will update the code for these metrics our repo later. Thanks!

from bayes-mil.

Hi, please refer to eval_froc.py for the patch-level evaluation metrics.

from bayes-mil.