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 of pathology foundation
models.

人工智能（AI）在大规模数字化临床数据集上训练系统以提高健康结果方面具有巨大潜力。计算病理学作为对诊断和生物标志物具有重大影响的大量显微镜图像数据，处于这一发展的前沿。百亿像素病理学切片由于其巨大的尺寸而面临独特挑战，通常被分成数万个较小的瓷砖进行分析。我们提出了一种新方法，通过在内存中全面高分辨率地同时训练瓷砖编码器和整个切片聚合器来解决这个问题，弥合了输入和切片级监督之间的差距。虽然计算成本更高，但详细的定量验证显示了病理基础模型的大规模预训练的前景。