Finer Disentanglement of Aleatoric Uncertainty Can Accelerate Chemical Histopathology Imaging

TL;DR

This paper introduces a novel adaptive imaging strategy that quickly identifies and selectively re-images high-uncertainty tissue regions to accelerate chemical histopathology imaging, improving segmentation accuracy.

Contribution

It presents the first fine-grained disentanglement method for aleatoric uncertainty in dynamic imaging, enabling targeted high-quality re-imaging in histopathology workflows.

Findings

Achieved improved downstream segmentation performance.

Demonstrated effective disentanglement of resolvable and irresolvable high-AU regions.

Streamlined infrared spectroscopic imaging of breast tissues.

Abstract

Label-free chemical imaging holds significant promise for improving digital pathology workflows, but data acquisition speed remains a limiting factor. To address this gap, we propose an adaptive strategy-initially scan the low information (LI) content of the entire tissue quickly, identify regions with high aleatoric uncertainty (AU), and selectively re-image them at better quality to capture higher information (HI) details. The primary challenge lies in distinguishing between high-AU regions mitigable through HI imaging and those that are not. However, since existing uncertainty frameworks cannot separate such AU subcategories, we propose a fine-grained disentanglement method based on post-hoc latent space analysis to unmix resolvable from irresolvable high-AU regions. We apply our approach to streamline infrared spectroscopic imaging of breast tissues, achieving superior downstream segmentation performance. This marks the first study focused on fine-grained AU disentanglement within dynamic image spaces (LI-to-HI), with novel application to streamline histopathology.