Efficient WSI classification with sequence reduction and transformers pretrained on text.

ABSTRACT: From computer vision to protein fold prediction, Language Models (LMs) have proven successful in transferring their representation of sequential data to a broad spectrum of tasks beyond the domain of natural language processing. Whole Slide Image (WSI) analysis in digital pathology naturally fits to transformer-based architectures. In a pre-processing step analogous to text tokenization, large microscopy images are tessellated into smaller image patches. However, due to the massive size of WSIs comprising thousands of such patches, the problem of WSI classification has not been addressed via deep transformer architectures, let alone via available text-pre-trained deep transformer language models. We introduce SeqShort, a multi-head attention-based sequence shortening layer that summarizes

SUBMITTER: Pisula JI 

PROVIDER: S-EPMC11829941 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature