Triage of 3D Pathology Data via 2.5D Multiple-instance Learning to Guide Pathologist Assessments

Gan Gao, Andrew H. Song, Fiona Wang, David Brenes, Rui Wang, Sarah S.L. Chow, Kevin W. Bishop, Lawrence D. True, Faisal Mahmood, Jonathan T.C. Liu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 6955-6965

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 data.