Learnable Token Sparsification for Efficient Gigapixel Whole Slide Image Reasoning
The processing of gigapixel whole slide images within vision language models faces a major difficulty due to an excessive number of visual tokens. Existing solutions typically rely on spatial downsampling or heuristic pruning strategies that operate without training, and these methods often discard subtle but clinically meaningful patterns because pathological evidence is scattered irregularly across the tissue. To overcome this limitation, we reformulate token reduction in whole slide images as a trainable sparsification problem, allowing the model to learn an optimal selection strategy instead of following fixed heuristics. We propose a decoupled routing architecture. To enable gradient...
| Authors | Jingzhi Chen, Landi He, Zhuo Chen et al. |
|---|---|
| Primary category | cs.CV |
| Published | 7 June 2026 |
| Last updated at source | 7 June 2026 |
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