Distributed Parallel Gradient Stacking(DPGS): Solving Whole Slide Image Stacking Challenge in Multi-Instance Learning
Abstract
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$\times$ 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.
Cite
Text
Wu et al. "Distributed Parallel Gradient Stacking(DPGS): Solving Whole Slide Image Stacking Challenge in Multi-Instance Learning." Proceedings of the 42nd International Conference on Machine Learning, 2025.Markdown
[Wu et al. "Distributed Parallel Gradient Stacking(DPGS): Solving Whole Slide Image Stacking Challenge in Multi-Instance Learning." Proceedings of the 42nd International Conference on Machine Learning, 2025.](https://mlanthology.org/icml/2025/wu2025icml-distributed/)BibTeX
@inproceedings{wu2025icml-distributed,
title = {{Distributed Parallel Gradient Stacking(DPGS): Solving Whole Slide Image Stacking Challenge in Multi-Instance Learning}},
author = {Wu, Boyuan and Wang, Zefeng and Lin, Xianwei and Xu, Jiachun and Yu, Jikai and Shicheng, Zhou and Chen, Hongda and Hu, Lianxin},
booktitle = {Proceedings of the 42nd International Conference on Machine Learning},
year = {2025},
pages = {67782-67792},
volume = {267},
url = {https://mlanthology.org/icml/2025/wu2025icml-distributed/}
}