Multi-Resolution Histopathology Patch Graphs for Ovarian Cancer Subtyping
<|reference_start|>Multi-Resolution Histopathology Patch Graphs for Ovarian Cancer Subtyping: Computer vision models are increasingly capable of classifying ovarian epithelial cancer subtypes, but they differ from pathologists by processing small tissue patches at a single resolution. Multi-resolution graph models leverage the spatial relationships of patches at multiple magnifications, learning the context for each patch. In this study, we conduct the most thorough validation of a graph model for ovarian cancer subtyping to date. Seven models were tuned and trained using five-fold cross-validation on a set of 1864 whole slide images (WSIs) from 434 patients treated at Leeds Teaching Hospitals NHS Trust. The cross-validation models were ensembled and evaluated using a balanced hold-out test set of 100 WSIs from 30 patients, and an external validation set of 80 WSIs from 80 patients in the Transcanadian Study. The best-performing model, a graph model using 10x+20x magnification data, gave balanced accuracies of 73%, 88%, and 99% in cross-validation, hold-out testing, and external validation, respectively. However, this only exceeded the performance of attention-based multiple instance learning in external validation, with a 93% balanced accuracy. Graph models benefitted greatly from using the UNI foundation model rather than an ImageNet-pretrained ResNet50 for feature extraction, with this having a much greater effect on performance than changing the subsequent classification approach. The accuracy of the combined foundation model and multi-resolution graph network offers a step towards the clinical applicability of these models, with a new highest-reported performance for this task, though further validations are still required to ensure the robustness and usability of the models.<|reference_end|>
@article{breen2024multi-resolution,
    title={Multi-Resolution Histopathology Patch Graphs for Ovarian Cancer
  Subtyping},
    author={Jack Breen, Katie Allen, Kieran Zucker, Nicolas M. Orsi, Nishant
  Ravikumar},
    journal={arXiv preprint arXiv:2407.18105},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.18105},
    primaryClass={eess.IV cs.AI cs.CV}
}