Abstract
Malignant ovarian tumors (OTs) are a leading cause of gynecological cancer deaths, and often remain asymptomatic until advanced stages, making early and accurate diagnosis crucial for effective treatment and good patient outcome. Current diagnostic methods often fall short due to the heterogeneous nature of OTs and the complexities in distinguishing benign from malignant forms. To overcome these limitations, this study proposes a novel framework leveraging transformer-based multiple-instance learning (MIL) and hierarchical self-supervised pre-training. To validate the model, a comprehensive multi-center dataset has been compiled, encompassing diverse patient demographics and imaging protocols. Benchmarking against conventional radiomics methods and other deep learning approaches, the hierarchical MIL model demonstrates superior performance with a median AUROC of 0.84 and high recall of 0.91. These results highlight significant improvements in sensitivity, essential for minimizing false negatives in clinical settings. The performed study emphasizes the importance of multi-center validation and external dataset testing to ensure generalization of the proposed model and obtain a higher robustness. The encountered complexity of multi-center data is found significant, since various clinical factors play an influential role. This makes baseline comparisons virtually impossible and the need for more multi-center research increasingly compelling and encouraging.
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Acknowledgements
We gratefully acknowledge the Catharina Hospital Eindhoven, the Amphia Hospital Breda, and The Dutch Cancer Institute - Antoni van Leeuwenhoek Hospital Amsterdam for their invaluable data collection support essential to this project.
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H.B. Claessens, C. et al. (2025). Multi-center Ovarian Tumor Classification Using Hierarchical Transformer-Based Multiple-Instance Learning. In: Ali, S., van der Sommen, F., Papież, B.W., Ghatwary, N., Jin, Y., Kolenbrander, I. (eds) Cancer Prevention, Detection, and Intervention. CaPTion 2024. Lecture Notes in Computer Science, vol 15199. Springer, Cham. https://doi.org/10.1007/978-3-031-73376-5_1
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