Diagnostic classification in toxicologic pathology using attention-guided weak supervision and whole slide image features: a pilot study in rat livers.

ABSTRACT: The diagnostic classification of digitized tissue images based on histopathologic lesions present in whole slide images (WSI) is a significant task that eludes modern image classification techniques. Even with advanced methods designed for digital histopathology, the domain of toxicologic pathology presents challenges in that histopathologic features may be at times complex, subtle, and/or rare. We propose an innovative weakly supervised learning method that leverages minimal annotations, a state-of-the-art self-supervised vision transformer for embedding extraction, and a novel guided attention mechanism that is better suited for heavily imbalanced datasets typical in toxicologic pathology. Our model demonstrates improvements in diagnostic classification and attention heatmap quality over

SUBMITTER: Zehnder P 

PROVIDER: S-EPMC11794696 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature