Finer Disentanglement of Aleatoric Uncertainty Can Accelerate Chemical Histopathology Imaging

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.