Tissue phenotyping is a fundamental computational pathology (CPath) task in
learning objective characterizations of histopathologic biomarkers in anatomic
pathology. However, whole-slide imaging (WSI) poses a complex computer vision
problem in which the large-scale image resolutions of WSIs and the enormous
diversity of morphological phenotypes preclude large-scale data annotation.
Current efforts have proposed using pretrained image encoders with either
transfer learning from natural image datasets or self-supervised pretraining on
publicly-available histopathology datasets, but have not been extensively
developed and evaluated across diverse tissue types at scale. We introduce UNI,
a general-purpose self-supervised model for pathology, pretrained using over
100 million tissue patches from over 100,000 diagnostic haematoxylin and
eosin-stained WSIs across 20 major tissue types, and evaluated on 33
representative CPath clinical tasks in CPath of varying diagnostic
difficulties. In addition to outperforming previous state-of-the-art models, we
demonstrate new modeling capabilities in CPath such as resolution-agnostic
tissue classification, slide classification using few-shot class prototypes,
and disease subtyping generalization in classifying up to 108 cancer types in
the OncoTree code classification system. UNI advances unsupervised
representation learning at scale in CPath in terms of both pretraining data and
downstream evaluation, enabling data-efficient AI models that can generalize
and transfer to a gamut of diagnostically-challenging tasks and clinical
workflows in anatomic pathology.

使用超过 100 万个组织样本预训练的自我监督模型 UNI，在计算病理学中取得了巨大的突破，具有分辨率无关的组织分类、少样本类别原型的幻灯片分类以及疾病亚型分类等新的建模能力，为解决解剖病理学中各种具有挑战性的任务和临床工作流提供了数据高效、泛化和迁移能力的人工智能模型。

一种面向计算病理学的通用自监督模型

A General-Purpose Self-Supervised Model for Computational Pathology

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

本文通过训练多种自监督模型，发现采用基于 DINO 知识蒸馏的 Vision Transformers 模型能够实现对组织形态学特征进行有效解释，在多个弱监督和补丁级任务上进行了验证。