Reusable specimen-level inference in computational pathology

Kaczmarzyk, Jakub, Sharma, Rishul, Koo, Peter, Saltz, Joel (January 2025) Reusable specimen-level inference in computational pathology. arXiv. ISSN 2331-8422 (Public Dataset) (Submitted)

PDF 10.48550.arXiv.2501.05945.pdf - Submitted Version Available under License Creative Commons Attribution Non-commercial Share Alike. Download (1MB)

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.

Item Type:	Paper
Subjects:	bioinformatics bioinformatics > computational biology
CSHL Authors:	Koo, Peter K Kaczmarzyk, Jakub
Communities:	CSHL labs > Koo Lab
SWORD Depositor:	CSHL Elements
Depositing User:	CSHL Elements
Date:	10 January 2025
Date Deposited:	24 Feb 2025 14:18
Last Modified:	24 Feb 2025 14:18
Dataset ID:	https://camelyon16.grand-challenge.org/ https://doi.org/10.7937/tcia.2019.3xbn2jcc
URI:	https://repository.cshl.edu/id/eprint/41801

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