Beyond Multiple Instance Learning: Full Resolution All-In-Memory End-To-End Pathology Slide Modeling
<|reference_start|>Beyond Multiple Instance Learning: Full Resolution All-In-Memory End-To-End Pathology Slide Modeling: Artificial Intelligence (AI) has great potential to improve health outcomes by training systems on vast digitized clinical datasets. Computational Pathology, with its massive amounts of microscopy image data and impact on diagnostics and biomarkers, is at the forefront of this development. Gigapixel pathology slides pose a unique challenge due to their enormous size and are usually divided into tens of thousands of smaller tiles for analysis. This results in a discontinuity in the machine learning process by separating the training of tile-level encoders from slide-level aggregators and the need to adopt weakly supervised learning strategies. Training models from entire pathology slides end-to-end has been largely unexplored due to its computational challenges. To overcome this problem, we propose a novel approach to jointly train both a tile encoder and a slide-aggregator fully in memory and end-to-end at high-resolution, bridging the gap between input and slide-level supervision. While more computationally expensive, detailed quantitative validation shows promise for large-scale pre-training and fine-tuning of pathology foundation models.<|reference_end|>
@article{campanella2024beyond,
    title={Beyond Multiple Instance Learning: Full Resolution All-In-Memory
  End-To-End Pathology Slide Modeling},
    author={Gabriele Campanella, Eugene Fluder, Jennifer Zeng, Chad Vanderbilt,
  Thomas J. Fuchs},
    journal={arXiv preprint arXiv:2403.04865},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.04865},
    primaryClass={eess.IV cs.CV}
}