{"version":"1.0","type":"rich","title":"Gist fb72ad147c42cdb900ded0f4b433b0f5","description":"This visualization evaluates lesion segmentation results by plotting false positives (FP) against false negatives (FN) for each exam. It retrieves subject data from a MongoDB collection, filters entries reviewed by a specific clinician, and aggregates counts of FP and FN annotations into a pandas DataFrame. The notebook uses matplotlib through `%pylab inline` for plotting, with a custom `remove_crap` function to clean chart spines and ticks. Data comes from the `subjects` collection in a local MongoDB instance.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-fb72ad147c42cdb900ded0f4b433b0f5?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}