Beyond Multiple Instance Learning: Full Resolution All-In-Memory
  End-To-End Pathology Slide Modeling
Gabriele Campanella Eugene Fluder Jennifer Zeng Chad Vanderbilt Thomas J. Fuchs
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.