Define and visualize occupancy statistic metrics#30
Draft
alex-l-kong wants to merge 18 commits into
Draft
Conversation
…le names on heatmap
… into occupancy_stats
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
What is the purpose of this PR?
Addresses first section of #27. Investigate the nature of occupancy statistics on the TNBC cohort.
How did you implement your changes
The first and most simple method to try is to define a grid of tiles on each image, then compute the number of cell counts in each. A cell is determined to fall within a tile if its centroid lies within its boundaries. Then, define a tile as positive if it falls above a certain threshold.
Perform grid search over number of tiles and positive threshold to see which one hits the sweet spot.
Remaining issues
If raw counts fail, then investigate distribution-related metrics such as entropy.