Artificial Intelligence for Digital and Computational Pathology
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866910298685833216 |
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| author | Song, Andrew H. Jaume, Guillaume Williamson, Drew F. K. Lu, Ming Y. Vaidya, Anurag Miller, Tiffany R. Mahmood, Faisal |
| author_facet | Song, Andrew H. Jaume, Guillaume Williamson, Drew F. K. Lu, Ming Y. Vaidya, Anurag Miller, Tiffany R. Mahmood, Faisal |
| contents | Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence, including deep learning, have boosted the field of computational pathology. This field holds tremendous potential to automate clinical diagnosis, predict patient prognosis and response to therapy, and discover new morphological biomarkers from tissue images. Some of these artificial intelligence-based systems are now getting approved to assist clinical diagnosis; however, technical barriers remain for their widespread clinical adoption and integration as a research tool. This Review consolidates recent methodological advances in computational pathology for predicting clinical end points in whole-slide images and highlights how these developments enable the automation of clinical practice and the discovery of new biomarkers. We then provide future perspectives as the field expands into a broader range of clinical and research tasks with increasingly diverse modalities of clinical data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_06148 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Artificial Intelligence for Digital and Computational Pathology Song, Andrew H. Jaume, Guillaume Williamson, Drew F. K. Lu, Ming Y. Vaidya, Anurag Miller, Tiffany R. Mahmood, Faisal Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Quantitative Methods Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence, including deep learning, have boosted the field of computational pathology. This field holds tremendous potential to automate clinical diagnosis, predict patient prognosis and response to therapy, and discover new morphological biomarkers from tissue images. Some of these artificial intelligence-based systems are now getting approved to assist clinical diagnosis; however, technical barriers remain for their widespread clinical adoption and integration as a research tool. This Review consolidates recent methodological advances in computational pathology for predicting clinical end points in whole-slide images and highlights how these developments enable the automation of clinical practice and the discovery of new biomarkers. We then provide future perspectives as the field expands into a broader range of clinical and research tasks with increasingly diverse modalities of clinical data. |
| title | Artificial Intelligence for Digital and Computational Pathology |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Quantitative Methods |
| url | https://arxiv.org/abs/2401.06148 |