Finer Disentanglement of Aleatoric Uncertainty Can Accelerate Chemical Histopathology Imaging

2025-02-27

AI Summary

To address the clinical bottlenecks in label-free chemical histopathological imaging—namely, slow data acquisition and the inability to distinguish optimizable from non-optimizable aleatoric uncertainty—this paper proposes an adaptive multi-scale infrared spectroscopic imaging method based on fine-grained decoupling of aleatoric uncertainty. Leveraging posterior latent-space modeling, it is the first to dynamically differentiate solvable from insolvable aleatoric uncertainty across the imaging space (from low- to high-information regions), thereby enabling targeted high-resolution re-scanning. Validated on breast tissue infrared spectral datasets, the method significantly improves tissue segmentation accuracy, achieves more efficient acquisition of high-information regions than random re-scanning baselines, and substantially reduces total imaging time—accelerating the clinical translation of digital pathology. Its core innovations lie in (i) discriminative classification of uncertainty subtypes according to their solvability, and (ii) a closed-loop, uncertainty-guided adaptive imaging decision framework.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Medical and Biological Imaging
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Label-free chemical imaging holds significant promise for improving digital pathology workflows. However, data acquisition speed remains a limiting factor for smooth clinical transition. 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 that can be mitigated through HI imaging and those that cannot. 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 efficiently image infrared spectroscopic data of breast tissues, achieving superior segmentation performance using the acquired HI data compared to a random baseline. This represents the first algorithmic study focused on fine-grained AU disentanglement within dynamic image spaces (LI-to-HI), with novel application to streamline histopathology.

Problem

• Improving chemical histopathology imaging speed and quality
• Distinguishing resolvable from irresolvable high aleatoric uncertainty regions
• Enhancing segmentation performance in breast tissue imaging

Innovation

• Adaptive scanning strategy for tissue imaging
• Fine-grained disentanglement of aleatoric uncertainty
• Post-hoc latent space analysis for AU resolution