Comparing ImageNet Pre-training with Digital Pathology Foundation Models for Whole Slide Image-Based Survival Analysis
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Abstract (arXiv)
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.
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References · 9
- 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
- 2018Attention-based Deep Multiple Instance Learningcited by 1,095
- 2021iBOT: Image BERT Pre-Training with Online Tokenizercited by 903
- 2023Vision Transformers Need Registerscited by 871
- 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 745
- 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 555
- 2019Using Self-Supervised Learning Can Improve Model Robustness and Uncertaintycited by 514
- 2016Revisiting Multiple Instance Neural Networkscited by 119
- 2020Studying The Effect of MIL Pooling Filters on MIL Taskscited by 3
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