Reusable specimen-level inference in computational pathology
This work addresses the problem of limited availability of specimen-level models for researchers in computational pathology, though it is incremental as it builds on existing foundation models.
The authors tackled the lack of accessible specimen-level models in computational pathology by developing SpinPath, a toolkit that includes pretrained models and inference tools, demonstrating its utility in metastasis detection across nine foundation models.
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.