# A Comprehensive Evaluation of Histopathology Foundation Models for Ovarian Cancer Subtype Classification arXiv: https://arxiv.org/abs/2405.09990 ยท HTML: https://mlsanity.com/paper/2405.09990/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Builds on 20 of its 71 references that are in the map. - First posted on arXiv 16 May 2024. ## Builds on (most cited first, 20 of 20) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023) - [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014) - [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023) - [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018) - [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020) - [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021) - [Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning](https://mlsanity.com/paper/2206.02647/) (2022) - [Self supervised contrastive learning for digital histopathology](https://mlsanity.com/paper/2011.13971/) (2020) - [Artificial Intelligence for Digital and Computational Pathology](https://mlsanity.com/paper/2401.06148/) (2023) - [Multiple Instance Learning for Digital Pathology: A Review on the State-of-the-Art, Limitations & Future Potential](https://mlsanity.com/paper/2206.04425/) (2022) - [Towards Large-Scale Training of Pathology Foundation Models](https://mlsanity.com/paper/2404.15217/) (2024) - [When an Image is Worth 1,024 x 1,024 Words: A Case Study in Computational Pathology](https://mlsanity.com/paper/2312.03558/) (2023) - [Benchmarking Pathology Feature Extractors for Whole Slide Image Classification](https://mlsanity.com/paper/2311.11772/) (2023) - [GRASP: GRAph-Structured Pyramidal Whole Slide Image Representation](https://mlsanity.com/paper/2402.03592/) (2024) - [Artificial Intelligence in Ovarian Cancer Histopathology: A Systematic Review](https://mlsanity.com/paper/2303.18005/) (2023) - [Efficient subtyping of ovarian cancer histopathology whole slide images using active sampling in multiple instance learning](https://mlsanity.com/paper/2302.08867/) (2023) - [Reducing Histopathology Slide Magnification Improves the Accuracy and Speed of Ovarian Cancer Subtyping](https://mlsanity.com/paper/2311.13956/) (2023) ## Built on by (most cited first, 13 of 13) - [PathBench: A comprehensive comparison benchmark for pathology foundation models towards precision oncology](https://mlsanity.com/paper/2505.20202/) (2025) - [Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts](https://mlsanity.com/paper/2410.06723/) (2024) - [Scanner-Induced Domain Shifts Undermine the Robustness of Pathology Foundation Models](https://mlsanity.com/paper/2601.04163/) (2026) - [Benchmarking Deep Learning and Vision Foundation Models for Atypical vs. Normal Mitosis Classification with Cross-Dataset Evaluation](https://mlsanity.com/paper/2506.21444/) (2025) - [TICON: A Slide-Level Tile Contextualizer for Histopathology Representation Learning](https://mlsanity.com/paper/2512.21331/) (2025) - [Benchmarking Foundation Models for Mitotic Figure Classification](https://mlsanity.com/paper/2508.04441/) (2025) - [Multi-Resolution Histopathology Patch Graphs for Ovarian Cancer Subtyping](https://mlsanity.com/paper/2407.18105/) (2024) - [Benchmarking Pathology Foundation Models for Breast Cancer Survival Prediction](https://mlsanity.com/paper/2604.24679/) (2026) - [Evaluation and Prognostic Validation of Deep Regression Models for WSI-Based Gene-Expression Prediction](https://mlsanity.com/paper/2410.00945/) (2024) - [Equi-ViT: Rotational Equivariant Vision Transformer for Robust Histopathology Analysis](https://mlsanity.com/paper/2601.09130/) (2026) - [From Patches to Patients: A study of the tile-to-slide performance transferability in Digital Pathology](https://mlsanity.com/paper/2606.10778/) (2026) - [Semantic-Aware Subgraph State Space Model for WSI Classification in Histopathology](https://mlsanity.com/paper/2609.03689/) (2026) - [Scaling Multi-Teacher Distillation for Digital Pathology](https://mlsanity.com/paper/2610.12574/) (2026) ## Abstract (arXiv) Large pretrained transformers are increasingly being developed as generalised foundation models which can underpin powerful task-specific artificial intelligence models. Histopathology foundation models show great promise across many tasks, but analyses have typically been limited by arbitrary hyperparameters that were not tuned to the specific task. We report the most rigorous single-task validation of histopathology foundation models to date, specifically in ovarian cancer morphological subtyping. Attention-based multiple instance learning classifiers were compared using three ImageNet-pretrained feature extractors and fourteen histopathology foundation models. The training set consisted of 1864 whole slide images from 434 ovarian carcinoma cases at Leeds Teaching Hospitals NHS Trust. Five-class classification performance was evaluated through five-fold cross-validation, and these cross-validation models were ensembled for hold-out testing and external validation on the Transcanadian Study and OCEAN Challenge datasets. The best-performing model used the H-optimus-0 foundation model, with five-class balanced accuracies of 89%, 97%, and 74% in the test sets. Normalisations and augmentations aided the performance of the ImageNet-pretrained ResNets, but these were still outperformed by 13 of the 14 foundation models. Hyperparameter tuning the downstream classifiers improved performance by a median 1.9% balanced accuracy, with many improvements being statistically significant. Histopathology foundation models offer a clear benefit to ovarian cancer subtyping, improving classification performance to a degree where clinical utility is tangible, albeit with an increased computational burden. Such models could provide a second opinion to histopathologists diagnosing challenging cases and may improve the accuracy, objectivity, and efficiency of pathological diagnoses overall.