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: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-01-01
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| Series: | npj Precision Oncology |
| Online Access: | https://doi.org/10.1038/s41698-024-00499-9 |
| _version_ | 1797356007444185088 |
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| author | Jakub R. Kaczmarzyk Alan O’Callaghan Fiona Inglis Swarad Gat Tahsin Kurc Rajarsi Gupta Erich Bremer Peter Bankhead Joel H. Saltz |
| author_facet | Jakub R. Kaczmarzyk Alan O’Callaghan Fiona Inglis Swarad Gat Tahsin Kurc Rajarsi Gupta Erich Bremer Peter Bankhead Joel H. Saltz |
| author_sort | Jakub R. Kaczmarzyk |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T14:20:27Z |
| format | Article |
| id | doaj.art-b7fa4472c6fb4be3a533684d16da2307 |
| institution | Directory Open Access Journal |
| issn | 2397-768X |
| language | English |
| last_indexed | 2024-03-08T14:20:27Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Precision Oncology |
| spelling | doaj.art-b7fa4472c6fb4be3a533684d16da23072024-01-14T12:07:24ZengNature Portfolionpj Precision Oncology2397-768X2024-01-01811510.1038/s41698-024-00499-9Open and reusable deep learning for pathology with WSInfer and QuPathJakub R. Kaczmarzyk0Alan O’Callaghan1Fiona Inglis2Swarad Gat3Tahsin Kurc4Rajarsi Gupta5Erich Bremer6Peter Bankhead7Joel H. Saltz8Department of Biomedical Informatics, Stony Brook UniversityCentre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, The University of EdinburghCentre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, The University of EdinburghDepartment of Biomedical Informatics, Stony Brook UniversityDepartment of Biomedical Informatics, Stony Brook UniversityDepartment of Biomedical Informatics, Stony Brook UniversityDepartment of Biomedical Informatics, Stony Brook UniversityCentre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, The University of EdinburghDepartment of Biomedical Informatics, Stony Brook UniversityAbstract 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.https://doi.org/10.1038/s41698-024-00499-9 |
| spellingShingle | Jakub R. Kaczmarzyk Alan O’Callaghan Fiona Inglis Swarad Gat Tahsin Kurc Rajarsi Gupta Erich Bremer Peter Bankhead Joel H. Saltz Open and reusable deep learning for pathology with WSInfer and QuPath npj Precision Oncology |
| title | Open and reusable deep learning for pathology with WSInfer and QuPath |
| title_full | Open and reusable deep learning for pathology with WSInfer and QuPath |
| title_fullStr | Open and reusable deep learning for pathology with WSInfer and QuPath |
| title_full_unstemmed | Open and reusable deep learning for pathology with WSInfer and QuPath |
| title_short | Open and reusable deep learning for pathology with WSInfer and QuPath |
| title_sort | open and reusable deep learning for pathology with wsinfer and qupath |
| url | https://doi.org/10.1038/s41698-024-00499-9 |
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