[2603.01143] TC-SSA: Token Compression via Semantic Slot Aggregation for Gigapixel Pathology Reasoning

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.01143 (cs) [Submitted on 1 Mar 2026] Title:TC-SSA: Token Compression via Semantic Slot Aggregation for Gigapixel Pathology Reasoning Authors:Zhuo Chen, Shawn Young, Lijian Xu View a PDF of the paper titled TC-SSA: Token Compression via Semantic Slot Aggregation for Gigapixel Pathology Reasoning, by Zhuo Chen and 2 other authors View PDF HTML (experimental) Abstract:The application of large vision-language models to computational pathology holds great promise for diagnostic assistants but faces a critical computational bottleneck: the gigapixel scale of Whole Slide Images (WSIs). A single WSI typically contains over 105 patches, creating sequence lengths that exceed the constraints of standard Transformer architectures. Existing solutions often resort to spatial sampling, which risks discarding diagnostically critical evidence. To address this, we propose TC-SSA (Token Compression via Semantic Slot Aggregation), a learnable token compression framework that aggregates patch features into a fixed number of semantic slots. A gated routing module assigns patches to slots using sparse Top-2 routing, followed by weighted aggregation, enabling global slide coverage under a strict token budget. The resulting representation retains diagnostically relevant information while reducing the number of visual tokens to 1.7% of the original sequence. On the SlideBench(TCGA), our model achieves 78.34% overall accuracy and ...