Reusable specimen-level inference in computational pathology

TL;DR

This paper introduces SpinPath, a toolkit that democratizes specimen-level inference in computational pathology by providing pretrained models and inference tools, thereby enhancing reproducibility and accelerating research adoption.

Contribution

The paper presents SpinPath, a comprehensive toolkit with pretrained models and inference platforms, addressing the lack of accessible specimen-level models in computational pathology.

Findings

Demonstrated utility in metastasis detection across nine foundation models

Facilitated reproducibility and simplified experimentation

Accelerated adoption of specimen-level deep learning

Abstract

Foundation models for computational pathology have shown great promise for specimen-level tasks and are increasingly accessible to researchers. However, specimen-level models built on these foundation models remain largely unavailable, hindering their broader utility and impact. To address this gap, we developed SpinPath, a toolkit designed to democratize specimen-level deep learning by providing a zoo of pretrained specimen-level models, a Python-based inference engine, and a JavaScript-based inference platform. We demonstrate the utility of SpinPath in metastasis detection tasks across nine foundation models. SpinPath may foster reproducibility, simplify experimentation, and accelerate the adoption of specimen-level deep learning in computational pathology research.