Discover and read the best of Twitter Threads about #interpretable
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[1/5] How can we improve pathologist efficiency in screening of normal colon biopsies? Take a look at our new #preprint that tackles this issue using #interpretable #AI 🔬 🚀 @PathLAKE_CoE
medrxiv.org/content/10.110…
medrxiv.org/content/10.110…
@PathLAKE_CoE @jnkath @AI4Pathology @NormanZerbe @anantm @mitkoveta @olkazuraw @GuillaumeJaume @pushpak_pati @jana_lipkova @nadeem_lab [2/5] Our approach, named #IGUANA, uses #pathologist-derived #interpretable features within a geometric learning framework to categorise biopsies as normal or abnormal (containing both non-neoplastic and neoplastic conditions).
[3/5] IGUANA achieves 0.98 AUC-ROC on an internal cohort and 0.97 AUC-ROC on three external cohorts. Our analysis shows that at a high sensitivity threshold of 99%, the proposed model can, on average, reduce the number of normal slides to be reviewed by a pathologist by 55%.
