Open and reusable deep learning for pathology with WSInfer and QuPath - Jakub R. Kaczmarzyk; Alan O’Callaghan; Fiona Inglis; Swarad Gat; Tahsin Kurc; Rajarsi Gupta; Erich Bremer; Peter Bankhead; Joel H. Saltz (npj Precision Oncology, 2024)
Abstract 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.