[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… @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).

[1/6] We are pleased to announce our paper ‘Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in
Histology Image Analysis’

paper: arxiv.org/abs/2004.03037
code: github.com/simongraham/ds…

@nmrajpoot @TIAwarwick [2/6] Our model is rotation-equivariant and therefore features transform as expected with rotation of the input.
This is especially important for histology image analysis, where images can appear at any orientation.