  Accurate patient diagnoses based on human tissue biopsies are hindered by
current clinical practice, where pathologists assess only a limited number of
thin 2D tissue slices sectioned from 3D volumetric tissue. Recent advances in
non-destructive 3D pathology, such as open-top light-sheet microscopy, enable
comprehensive imaging of spatially heterogeneous tissue morphologies, offering
the feasibility to improve diagnostic determinations. A potential early route
towards clinical adoption for 3D pathology is to rely on pathologists for final
diagnosis based on viewing familiar 2D H&E-like image sections from the 3D
datasets. However, manual examination of the massive 3D pathology datasets is
infeasible. To address this, we present CARP3D, a deep learning triage approach
that automatically identifies the highest-risk 2D slices within 3D volumetric
biopsy, enabling time-efficient review by pathologists. For a given slice in
the biopsy, we estimate its risk by performing attention-based aggregation of
2D patches within each slice, followed by pooling of the neighboring slices to
compute a context-aware 2.5D risk score. For prostate cancer risk
stratification, CARP3D achieves an area under the curve (AUC) of 90.4% for
triaging slices, outperforming methods relying on independent analysis of 2D
sections (AUC=81.3%). These results suggest that integrating additional depth
context enhances the model's discriminative capabilities. In conclusion, CARP3D
has the potential to improve pathologist diagnosis via accurate triage of
high-risk slices within large-volume 3D pathology datasets.
