text:   Histopathology",6,"['Excited to share work with @rahulgk @MSFTResearch, presented at #LMRL #NeurIPS 2021.\n\nWe pretrained ViTs on histopathology images - and find they learn meaningful visual concepts.\n\nPaper Link: <LINK>\nPretrained Weights: <LINK>\n\nKey Findings: 1/ <LINK>', 'In general CV, DINO by @mcaron31 and extensions can learn interpretable subparts of images, and has been used for object discovery.\n\nWe highlight interpreting images as subparts, e.g. - part-whole hierarchies, is very natural in histology in learning cell-tissue organization. 2/ https://t.co/HTL54DtTcC', 'Comparing w/ SimCLR and ImageNet features, DINO learns better &amp; more efficient representations, tested on patch- and slide-level tasks.\n\nSSL helps w/ domain shift. On raw &amp; stain-normalized CRC100K, global structure of morphological subtypes are better preserved than ImageNet. 3/ https://t.co/MxPJk1KaYq', 'Lastly, DINO localizes cell location quite well w/o supervision. Our findings demonstrate ViTs can easily localize visual concepts in histopathology via introspecting the attention heads. 4/ https://t.co/mZ3Xk4UXoJ', 'We plan to add more pretrained models + evaluation metrics, with a larger paper coming soon :^). Special thanks to also the BioML Group @MSRNE, @lorin_crawford, @apsoleimany, Kristen Severson, @KevinKaichuang, @nfusi, and @rahulgk again for supporting me over the summer! 5/', '@tae_hwang @rahulgk @MSFTResearch Thank you Tae!']",22,03,1343