Triage of 3D pathology data via 2.5D multiple-instance learning to guide pathologist assessments

• URL: http://arxiv.org/abs/2406.07061v1
• Date: Tue, 11 Jun 2024 08:42:07 GMT
• Title: Triage of 3D pathology data via 2.5D multiple-instance learning to guide pathologist assessments
• Authors: 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,
• Abstract summary: We present CARP3D, a deep learning triage approach that automatically identifies the highest-risk 2D slices within 3D volumetric biopsy. 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.
• Score: 7.735043623909641
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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