Reusable specimen-level inference in computational pathology

Jakub R. Kaczmarzyk; Rishul Sharma; Peter K. Koo; Joel H. Saltz

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arXiv · 2501.05945

Reusable specimen-level inference in computational pathology

Jakub R. Kaczmarzyk·Rishul Sharma·Peter K. Koo·Joel H. Saltz

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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.

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Jakub R. Kaczmarzyk, Rishul Sharma, Peter K. Koo, Joel H. Saltz. 2025-01-10. Reusable specimen-level inference in computational pathology. https://arxiv.org/abs/2501.05945

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