Kaczmarzyk Jakub R, O'Callaghan Alan, Inglis Fiona, Gat Swarad, Kurc Tahsin, Gupta Rajarsi, Bremer Erich, Bankhead Peter, Saltz Joel H
Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA.
Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK.
NPJ Precis Oncol. 2024 Jan 10;8(1):9. doi: 10.1038/s41698-024-00499-9.
Digital pathology has seen a proliferation of deep learning models in recent years, but many models are not readily reusable. To address this challenge, we developed WSInfer: an open-source software ecosystem designed to streamline the sharing and reuse of deep learning models for digital pathology. The increased access to trained models can augment research on the diagnostic, prognostic, and predictive capabilities of digital pathology.
近年来，数字病理学领域深度学习模型大量涌现，但许多模型难以直接复用。为应对这一挑战，我们开发了WSInfer：一个开源软件生态系统，旨在简化数字病理学深度学习模型的共享与复用。对经过训练的模型的更多访问可以加强对数字病理学诊断、预后和预测能力的研究。
