Reusable specimen-level inference in computational pathology
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866909453783138304 |
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| author | Kaczmarzyk, Jakub R. Sharma, Rishul Koo, Peter K. Saltz, Joel H. |
| author_facet | Kaczmarzyk, Jakub R. Sharma, Rishul Koo, Peter K. Saltz, Joel H. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_05945 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reusable specimen-level inference in computational pathology Kaczmarzyk, Jakub R. Sharma, Rishul Koo, Peter K. Saltz, Joel H. Image and Video Processing Computer Vision and Pattern Recognition Tissues and Organs 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. |
| title | Reusable specimen-level inference in computational pathology |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Tissues and Organs |
| url | https://arxiv.org/abs/2501.05945 |