Open and reusable deep learning for pathology with WSInfer and QuPath
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 p...
Main Authors: | Jakub R. Kaczmarzyk, Alan O’Callaghan, Fiona Inglis, Swarad Gat, Tahsin Kurc, Rajarsi Gupta, Erich Bremer, Peter Bankhead, Joel H. Saltz |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2024-01-01
|
Series: | npj Precision Oncology |
Online Access: | https://doi.org/10.1038/s41698-024-00499-9 |
Similar Items
-
QuPath digital immunohistochemical analysis of placental tissue
by: Ashley L Hein, et al.
Published: (2021-01-01) -
QuPath: The global impact of an open source digital pathology system
by: M.P. Humphries, et al.
Published: (2021-01-01) -
QuPath Algorithm Accurately Identifies MLH1-Deficient Inflammatory Bowel Disease-Associated Colorectal Cancers in a Tissue Microarray
by: Ross J. Porter, et al.
Published: (2023-05-01) -
Digital image analysis of immunohistochemistry KI-67 using QuPath software in breast cancer
by: Hermin Aminah Usman, et al.
Published: (2021-04-01) -
An Open-Source Whole Slide Image Registration Workflow at Cellular Precision Using Fiji, QuPath and Elastix
by: Nicolas Chiaruttini, et al.
Published: (2022-01-01)