The abundance of information present in Whole Slide Images (WSIs) renders them an essential tool for survival analysis. Several Multiple Instance Learning frameworks proposed for this task utilize a ResNet50 backbone pre-trained on natural images. By leveraging recenetly released histopathological foundation models such as UNI and Hibou, the predictive prowess of existing MIL networks can be enhanced. Furthermore, deploying an ensemble of digital pathology foundation models yields higher baseline accuracy, although the benefits appear to diminish with more complex MIL architectures. Our code will be made publicly available upon acceptance.

翻译：全切片图像（WSIs）蕴含的丰富信息使其成为生存分析的重要工具。针对此任务提出的多种多示例学习框架通常采用在自然图像上预训练的ResNet50骨干网络。通过利用近期发布的组织病理学基础模型（如UNI和Hibou），可以增强现有MIL网络的预测能力。此外，部署数字病理学基础模型集成能获得更高的基线准确率，尽管在更复杂的MIL架构中这种优势似乎会减弱。我们的代码将在论文录用后公开。