CrossPath: Top-down, Cross Data Type, Multi-Criterion Histological Analysis by Shepherding Mixed AI Models

by   Hongyan Gu, et al.

Data-driven AI promises support for pathologists to discover sparse tumor patterns in high-resolution histological images. However, three limitations prevent AI from being adopted into clinical practice: (i) a lack of comprehensiveness where most AI algorithms only rely on single criteria/examination; (ii) a lack of explainability where AI models work as 'black-boxes' with little transparency; (iii) a lack of integrability where it is unclear how AI can become part of pathologists' existing workflow. To address these limitations, we propose CrossPath: a brain tumor grading tool that supports top-down, cross data type, multi-criterion histological analysis, where pathologists can shepherd mixed AI models. CrossPath first uses AI to discover multiple histological criteria with H and E and Ki-67 slides based on WHO guidelines. Second, CrossPath demonstrates AI findings with multi-level explainable supportive evidence. Finally, CrossPath provides a top-down shepherding workflow to help pathologists derive an evidence-based, precise grading result. To validate CrossPath, we conducted a user study with pathologists in a local medical center. The result shows that CrossPath achieves a high level of comprehensiveness, explainability, and integrability while reducing about one-third time consumption compared to using a traditional optical microscope.


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