Whole Slide Image Survival Analysis Using Histopathological Feature Extractors
<|reference_start|>Whole Slide Image Survival Analysis Using Histopathological Feature Extractors: The abundance of information present in Whole Slide Images (WSIs) makes them useful for prognostic evaluation. A large number of models utilizing a pretrained ResNet backbone have been released and employ various feature aggregation techniques, primarily based on Multiple Instance Learning (MIL). By leveraging the recently released UNI feature extractor, existing models can be adapted to achieve higher accuracy, which paves the way for more robust prognostic tools in digital pathology.<|reference_end|>
@article{papadopoulos2024comparing,
    title={Comparing ImageNet Pre-training with Digital Pathology Foundation Models
  for Whole Slide Image-Based Survival Analysis},
    author={Kleanthis Marios Papadopoulos},
    journal={arXiv preprint arXiv:2405.17446},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.17446},
    primaryClass={eess.IV cs.CV}
}