SEW: Self-calibration Enhanced Whole Slide Pathology Image Analysis
Journal:
arXiv
Published Date:
Dec 14, 2024
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.
