新闻
SLICEChat: Progressive In-Encoder Token Pruning for Whole-Slide Pathology Language Models
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- SLICEChat introduces progressive token pruning within a hybrid Mamba-Transformer encoder for processing gigapixel pathology images with language models.
- 为何重要
- Matters for engineers building medical AI systems that analyze whole-slide pathology images and need to balance accuracy with computational efficiency.
- 注意
- Results are on specific pathology datasets; generalization to other gigapixel image domains and real-world deployment constraints remain unclear.
- llm
- language model
- encoder
- attention
- token
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- Multimodal Context Integration
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