  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.
