超越多实例学习：全分辨率全内存端到端病理切片建模
Beyond Multiple Instance Learning: Full Resolution All-In-Memory End-To-End Pathology Slide Modeling
AI总结：
针对病理切片端到端训练的计算难题，提出全内存高分辨率联合训练图块编码器与切片聚合器的新方法，弥合监督差距，验证显示对预训练与微调有前景。
摘要：
人工智能（AI）通过在海量数字化临床数据集上训练系统，具有改善健康结果的巨大潜力。计算病理学拥有海量显微镜图像数据，并对诊断和生物标志物产生影响，正处于这一发展的前沿。十亿像素级别的病理切片因其巨大的尺寸而构成独特挑战，通常被分割成数万个小图块进行分析。这导致机器学习过程中的不连续性，将图块级编码器的训练与切片级聚合器分开，并需要采用弱监督学习策略。由于计算挑战，从整个病理切片端到端训练模型在很大程度上尚未被探索。为克服这一问题，我们提出了一种新方法，即在内存中联合训练图块编码器和切片聚合器，并以高分辨率端到端进行，弥合输入与切片级监督之间的差距。尽管计算成本更高，详细的定量验证显示该方法在大规模预训练和病理基础模型微调方面具有前景。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
