SAMPLER: unsupervised representations for rapid analysis of whole slide tissue images.

ABSTRACT:

Background

Deep learning has revolutionized digital pathology, allowing automatic analysis of hematoxylin and eosin (H&E) stained whole slide images (WSIs) for diverse tasks. WSIs are broken into smaller images called tiles, and a neural network encodes each tile. Many recent works use supervised attention-based models to aggregate tile-level features into a slide-level representation, which is then used for downstream analysis. Training supervised attention-based models is computationally intensive, architecture optimization of the attention module is non-trivial, and labeled data are not always available. Therefore, we developed an unsupervised and fast approach called SAMPLER to generate slide-level representations.

Methods

Slide-level representations of SAMPLER are gener

SUBMITTER: Mukashyaka P 

PROVIDER: S-EPMC10733087 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature