Finer Disentanglement of Aleatoric Uncertainty Can Accelerate Chemical Histopathology Imaging
This work addresses the speed limitation in chemical histopathology imaging for digital pathology workflows, presenting an incremental improvement through fine-grained uncertainty disentanglement.
The paper tackled the problem of slow data acquisition in label-free chemical imaging for digital pathology by proposing an adaptive strategy that quickly scans low-information content, identifies high aleatoric uncertainty regions, and selectively re-images them at higher quality, achieving superior downstream segmentation performance in breast tissue imaging.
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.