SEW: Self-calibration Enhanced Whole Slide Pathology Image Analysis

TL;DR

This paper introduces a self-calibration enhanced framework for whole slide pathology image analysis that effectively combines global and local features, improving accuracy and interpretability in cancer diagnosis.

Contribution

The novel framework integrates global classification, focus prediction, and detailed analysis with feature consistency constraints for comprehensive pathology image analysis.

Findings

Achieves rapid, accurate, and explainable pathology grading and prognosis.

Effectively identifies relevant regions for detailed analysis.

Uncovers novel prognostic tumor markers through feature analysis.

Abstract

Pathology images are considered the ``gold standard" for cancer diagnosis and treatment, with gigapixel images providing extensive tissue and cellular information. Existing methods fail to simultaneously extract global structural and local detail features for comprehensive pathology image analysis efficiently. To address these limitations, we propose a self-calibration enhanced framework for whole slide pathology image analysis, comprising three components: a global branch, a focus predictor, and a detailed branch. The global branch initially classifies using the pathological thumbnail, while the focus predictor identifies relevant regions for classification based on the last layer features of the global branch. The detailed extraction branch then assesses whether the magnified regions correspond to the lesion area. Finally, a feature consistency constraint between the global and detail branches ensures that the global branch focuses on the appropriate region and extracts sufficient discriminative features for final identification. These focused discriminative features prove invaluable for uncovering novel prognostic tumor markers from the perspective of feature cluster uniqueness and tissue spatial distribution. Extensive experiment results demonstrate that the proposed framework can rapidly deliver accurate and explainable results for pathological grading and prognosis tasks.