text: 90,222,1499812487193051137,18647972,Richard J. Chen,"Excited to share work with @rahulgk @MSFTResearch, presented at #LMRL #NeurIPS 2021. We pretrained ViTs on histopathology images - and find they learn meaningful visual concepts. Paper Link: <LINK> Pretrained Weights: <LINK> Key Findings: 1/ <LINK> In general CV, DINO by @mcaron31 and extensions can learn interpretable subparts of images, and has been used for object discovery. We highlight interpreting images as subparts, e.g. - part-whole hierarchies, is very natural in histology in learning cell-tissue organization. 2/ <LINK> Comparing w/ SimCLR and ImageNet features, DINO learns better & more efficient representations, tested on patch- and slide-level tasks. SSL helps w/ domain shift. On raw & stain-normalized CRC100K, global structure of morphological subtypes are better preserved than ImageNet. 3/ <LINK> 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/ <LINK> 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!",http://arxiv.org/abs/2203.00585,"Tissue phenotyping is a fundamental task in learning objective characterizations of histopathologic biomarkers within the tumor-immune microenvironment in cancer pathology. However, whole-slide imaging (WSI) is a complex computer vision in which: 1) WSIs have enormous image resolutions with precludes large-scale pixel-level efforts in data curation, and 2) diversity of morphological phenotypes results in inter- and intra-observer variability in tissue labeling. To address these limitations, current efforts have proposed using pretrained image encoders (transfer learning from ImageNet, self-supervised pretraining) in extracting morphological features from pathology, but have not been extensively validated. In this work, we conduct a search for good representations in pathology by training a variety of self-supervised models with validation on a variety of weakly-supervised and patch-level tasks. Our key finding is in discovering that Vision Transformers using DINO-based knowledge distillation are able to learn data-efficient and interpretable features in histology images wherein the different attention heads learn distinct morphological phenotypes. We make evaluation code and pretrained weights publicly-available at: this https URL ","Self-Supervised Vision Transformers Learn Visual Concepts in