Self-supervised learning (SSL) has been successful in building patch
embeddings of small histology images (e.g., 224x224 pixels), but scaling these
models to learn slide embeddings from the entirety of giga-pixel whole-slide
images (WSIs) remains challenging. Here, we leverage complementary information
from gene expression profiles to guide slide representation learning using
multimodal pre-training. Expression profiles constitute highly detailed
molecular descriptions of a tissue that we hypothesize offer a strong
task-agnostic training signal for learning slide embeddings. Our slide and
expression (S+E) pre-training strategy, called Tangle, employs
modality-specific encoders, the outputs of which are aligned via contrastive
learning. Tangle was pre-trained on samples from three different organs: liver
(n=6,597 S+E pairs), breast (n=1,020), and lung (n=1,012) from two different
species (Homo sapiens and Rattus norvegicus). Across three independent test
datasets consisting of 1,265 breast WSIs, 1,946 lung WSIs, and 4,584 liver
WSIs, Tangle shows significantly better few-shot performance compared to
supervised and SSL baselines. When assessed using prototype-based
classification and slide retrieval, Tangle also shows a substantial performance
improvement over all baselines. Code available at
this https URL

使用自监督学习和基因表达谱进行多模态预训练，Tangle 可以在组织学图像中构建幻灯片嵌入，并且在性能上优于有监督和无监督基线模型。