DOI: 10.1101/19003897Aug 13, 2019Paper

PathFlowAI: A Convenient High-Throughput Workflow for Preprocessing, Deep Learning Analytics and Interpretation in Digital Pathology

MedRxiv : the Preprint Server for Health Sciences
Joshua J LevyLouis J Vaickus

Abstract

The diagnosis of disease often requires analysis of a biopsy. Many diagnoses depend not only on the presence of certain features but on their location within the tissue. Recently, a number of deep learning diagnostic aids have been developed to classify digitized biopsy slides. Clinical workflows often involve processing of more than 500 slides per day. But, clinical use of deep learning diagnostic aids would require a preprocessing workflow that is cost-effective, flexible, scalable, rapid, interpretable, and transparent. Here, we present such a workflow, optimized using Dask and mixed precision training via APEX, capable of handling any patch-level or slide level classification and prediction problem. The workflow uses a flexible and fast preprocessing and deep learning analytics pipeline, incorporates model interpretation and has a highly storage-efficient audit trail. We demonstrate the utility of this package on the analysis of a prototypical anatomic pathology specimen, liver biopsies for evaluation of hepatitis from a prospective cohort. The preliminary data indicate that PathFlowAI may become a cost-effective and time-efficient tool for clinical use of Artificial Intelligence (AI) algorithms.

Software Mentioned

QuPath
Common Workflow Language
Linux
Pytorch
SHAP
NPY
ASAP
SQL
PathFlowAI
GitHub

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