Whole Slide Image (WSI) analysis is framed as a Multiple Instance Learning (MIL) problem, but existing methods struggle with non-stackable data due to inconsistent instance lengths, which degrades performance and efficiency. We propose a Distributed Parallel Gradient Stacking (DPGS) framework with Deep Model-Gradient Compression (DMGC) to address this. DPGS enables lossless MIL data stacking for the first time, while DMGC accelerates distributed training via joint gradient-model compression. Experiments on Camelyon16 and TCGA-Lung datasets demonstrate up to 31× faster training, up to a 99.2% reduction in model communication size at convergence, and up to a 9.3% improvement in accuracy compared to the baseline. To our knowledge, this is the first work to solve non-stackable data in MIL while improving both speed and accuracy.

Whole Slide Images (WSIs) are large medical images used in cancer diagnosis. They are split into many patches and analyzed using Multiple Instance Learning (MIL). But since each image has a different number of patches, current methods can’t train in batches, making them slow and less accurate. We propose DPGS, a method that enables fast, parallel training on uneven data. We also introduce DMGC, which cuts communication costs by over 99%. Tested on cancer datasets, our method sped up training by up to 31× and improved accuracy by 9.3%, making AI tools for pathology faster and more reliable.