Tissue Concepts v2: A Supervised Foundation Model For Whole Slide Images
Journal:
arXiv
Published Date:
Jul 8, 2025
Abstract
Foundation models (FMs) are transforming the field of computational pathology
by offering new approaches to analyzing histopathology images. Typically
relying on weeks of training on large databases, the creation of FMs is a
resource-intensive process in many ways. In this paper, we introduce the
extension of our supervised foundation model, Tissue Concepts, to whole slide
images, called Tissue Concepts v2 (TCv2), a supervised foundation model for
whole slide images to address the issue above. TCv2 uses supervised, end-to-end
multitask learning on slide-level labels. Training TCv2 uses a fraction of the
training resources compared to self-supervised training. The presented model
shows superior performance compared to SSL-trained models in cancer subtyping
benchmarks and is fully trained on freely available data. Furthermore, a shared
trained attention module provides an additional layer of explainability across
different tasks.
