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Artificial intelligence has found increasing use for ovarian cancermorphological subtyping from histopathology slides, but the optimalmagnification for computational interpretation is unclear. Highermagnifications offer abundant cytological information, whereas lowermagnifications give a broader histoarchitectural overview. Usingattention-based multiple instance learning, we performed the most extensiveanalysis of ovarian cancer tissue magnifications to date, with data at sixmagnifications 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 intermediatemagnifications (5x and 10x) performed best in hold-out testing (62% and 61%accuracy, respectively). Lower magnification models were also significantlyfaster, with the 5x model taking 5% as long to train and 31% as long toevaluate slides compared to 40x. This indicates that the standard usage of highmagnifications for computational ovarian cancer subtyping may be unnecessary,with lower magnifications giving faster, more accurate alternatives.

Reducing Histopathology Slide Magnification Improves the Accuracy and Speed of Ovarian Cancer Subtyping