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Reusable specimen-level inference in computational pathology.

Jakub R Kaczmarzyk1,2,3, Rishul Sharma1,4, Peter K Koo1,2

  • 1Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA.

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SpinPath democratizes specimen-level deep learning in computational pathology by offering pretrained models and tools. This toolkit accelerates research and adoption of advanced deep learning for pathology tasks.

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Area of Science:

  • Computational pathology
  • Deep learning
  • Artificial intelligence in medicine

Background:

  • Foundation models show promise for computational pathology tasks.
  • Specimen-level models based on foundation models are not widely available, limiting research utility.
  • There is a need for accessible tools to leverage foundation models for specimen-level pathology analysis.

Purpose of the Study:

  • To develop SpinPath, a toolkit to democratize specimen-level deep learning in computational pathology.
  • To provide researchers with a zoo of pretrained specimen-level models.
  • To enable easier experimentation and adoption of deep learning for pathology.

Main Methods:

  • Developed SpinPath, a toolkit comprising pretrained specimen-level models, a Python inference engine, and a JavaScript inference platform.
  • Evaluated SpinPath's utility in metastasis detection tasks.
  • Tested across nine different foundation models.

Main Results:

  • SpinPath successfully demonstrated utility in metastasis detection tasks.
  • The toolkit provides a diverse set of pretrained specimen-level models.
  • The Python and JavaScript platforms facilitate accessible inference.

Conclusions:

  • SpinPath democratizes specimen-level deep learning in computational pathology.
  • The toolkit can foster reproducibility and simplify experimentation.
  • SpinPath is expected to accelerate the adoption of specimen-level deep learning in pathology research.