Reducing Histopathology Slide Magnification Improves the Accuracy and Speed of Ovarian Cancer Subtyping
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866929560133566464 |
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| author | Breen, Jack Allen, Katie Zucker, Kieran Orsi, Nicolas M. Ravikumar, Nishant |
| author_facet | Breen, Jack Allen, Katie Zucker, Kieran Orsi, Nicolas M. Ravikumar, Nishant |
| contents | Artificial intelligence has found increasing use for ovarian cancer morphological subtyping from histopathology slides, but the optimal magnification for computational interpretation is unclear. Higher magnifications offer abundant cytological information, whereas lower magnifications give a broader histoarchitectural overview. Using attention-based multiple instance learning, we performed the most extensive analysis of ovarian cancer tissue magnifications to date, with data at six magnifications subjected to the same preprocessing, hyperparameter tuning, cross-validation and hold-out testing procedures. The lowest magnifications (1.25x and 2.5x) performed best in cross-validation, and intermediate magnifications (5x and 10x) performed best in hold-out testing (62% and 61% accuracy, respectively). Lower magnification models were also significantly faster, with the 5x model taking 5% as long to train and 31% as long to evaluate slides compared to 40x. This indicates that the standard usage of high magnifications for computational ovarian cancer subtyping may be unnecessary, with lower magnifications giving faster, more accurate alternatives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_13956 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Reducing Histopathology Slide Magnification Improves the Accuracy and Speed of Ovarian Cancer Subtyping Breen, Jack Allen, Katie Zucker, Kieran Orsi, Nicolas M. Ravikumar, Nishant Image and Video Processing Artificial intelligence has found increasing use for ovarian cancer morphological subtyping from histopathology slides, but the optimal magnification for computational interpretation is unclear. Higher magnifications offer abundant cytological information, whereas lower magnifications give a broader histoarchitectural overview. Using attention-based multiple instance learning, we performed the most extensive analysis of ovarian cancer tissue magnifications to date, with data at six magnifications subjected to the same preprocessing, hyperparameter tuning, cross-validation and hold-out testing procedures. The lowest magnifications (1.25x and 2.5x) performed best in cross-validation, and intermediate magnifications (5x and 10x) performed best in hold-out testing (62% and 61% accuracy, respectively). Lower magnification models were also significantly faster, with the 5x model taking 5% as long to train and 31% as long to evaluate slides compared to 40x. This indicates that the standard usage of high magnifications for computational ovarian cancer subtyping may be unnecessary, with lower magnifications giving faster, more accurate alternatives. |
| title | Reducing Histopathology Slide Magnification Improves the Accuracy and Speed of Ovarian Cancer Subtyping |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2311.13956 |