Triage of 3D pathology data via 25D multiple-instance learning to guide pathologist assessments
<|reference_start|>Triage of 3D pathology data via 25D multiple-instance learning to guide pathologist assessments: 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.<|reference_end|>
@article{gao2024triage,
    title={Triage of 3D pathology data via 2.5D multiple-instance learning to guide
  pathologist assessments},
    author={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},
    journal={Proceedings of the IEEE/CVF Conference on Computer Vision and
  Pattern Recognition (CVPR) Workshops, 2024, pp. 6955-6965},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.07061},
    primaryClass={eess.IV cs.CV}
}