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@@ -21,5 +21,52 @@
 ## Screenshots (if appropriate):
 
 
+## Metrics (if appropriate):
+
+If you submitting a PR for a prediction algorithm (segmentation, identification,
+or classification) please fill in values for as many as the below statistics as
+are relevant.
+
+*algorithms by metric*
+
+metric | relevant algorithms
+-------|--------------------
+[accuracy <sup>1</sup> <sup>2</sup>](https://stats.stackexchange.com/a/231237/143678) | classification, identification
+[data IO](https://unix.stackexchange.com/questions/55212) | classification, identification, segmentation
+[Dice coefficient <sup>3</sup>](https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient) | segmentation
+[disk space usage](https://www.cyberciti.biz/faq/linux-check-disk-space-command) | classification, identification, segmentation
+[Hausdorff distance <sup>3</sup>](https://en.wikipedia.org/wiki/Hausdorff_distance) | segmentation
+[Jaccard index](https://en.wikipedia.org/wiki/Jaccard_index) | segmentation
+[Log Loss](http://wiki.fast.ai/index.php/Log_Loss) | classification, identification <sup>4</sup>
+[memory usage](https://stackoverflow.com/questions/110259) | classification, identification, segmentation
+[prediction time <sup>2</sup>](https://stackoverflow.com/questions/385408) | classification, identification, segmentation
+[sensitivity <sup>3</sup>](http://wiki.fast.ai/index.php/Deep_Learning_Glossary#Recall) | segmentation
+[specificity <sup>3</sup>](http://wiki.fast.ai/index.php/Deep_Learning_Glossary#Specificity) | segmentation
+[training time <sup>2</sup>](https://stackoverflow.com/questions/385408) | classification, identification, segmentation
+
+*notes*
+
+1. Use 5-fold cross validation if there is enough time and computational power available, otherwise use a holdout set
+1. This metric may be automatically calculated by the machine learning architecture, e.g., Keras
+1. The calculations for these metrics [are available here](https://github.com/concept-to-clinic/concept-to-clinic/blob/master/prediction/src/algorithms/segment/src/evaluate.py)
+1. In order to calculate Log Loss for identification, the data needs to be arranged in a way that shows for each pixel, whether or not it is a nodule centriod. Restated, the pixel level labels of 1/0 would correspond to centriod/not-centriod.
+
+*metrics by algorithm*
+
+algorithm      | relevant metrics
+---------------|------------------
+classification | accuracy, data IO, disk space usage, Log Loss, memory usage, prediction time, training time
+identification | accuracy, data IO, disk space usage, Log Loss, memory usage, prediction time, training time
+segmentation   | data IO, Dice coefficient, disk space usage, Hausdorff distance, Jaccard index, memory usage, prediction time, sensitivity, specificity, training time
+
+When reporting your values, please use a format similar to the following example.
+
+algorithm    | metric | value
+-------------|--------|------:
+segmentation | accuracy | 99.5
+segmentation | Jaccard index | 0.5
+segmentation | prediction time (s) | 45.3
+segmentation | memory usage (MB) | 5.4
+
 ## CLA
-- [ ] I have signed the CLA; if other committers are in the commit history, they have signed the CLA as well
\ No newline at end of file
+- [ ] I have signed the CLA; if other committers are in the commit history, they have signed the CLA as well