Prompt-based Dynamic Token Pruning for Efficient Segmentation of Medical Images
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908504169644032 |
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| author | Dutta, Pallabi Maity, Anubhab Mitra, Sushmita |
| author_facet | Dutta, Pallabi Maity, Anubhab Mitra, Sushmita |
| contents | The high computational demands of Vision Transformers (ViTs) in processing a large number of tokens often constrain their practical application in analyzing medical images. This research proposes a Prompt-driven Adaptive Token ({\it PrATo}) pruning method to selectively reduce the processing of irrelevant tokens in the segmentation pipeline. The prompt-based spatial prior helps to rank the tokens according to their relevance. Tokens with low-relevance scores are down-weighted, ensuring that only the relevant ones are propagated for processing across subsequent stages. This data-driven pruning strategy improves segmentation accuracy and inference speed by allocating computational resources to essential regions. The proposed framework is integrated with several state-of-the-art models to facilitate the elimination of irrelevant tokens, thereby enhancing computational efficiency while preserving segmentation accuracy. The experimental results show a reduction of $\sim$ 35-55% tokens; thus reducing the computational costs relative to baselines. Cost-effective medical image processing, using our framework, facilitates real-time diagnosis by expanding its applicability in resource-constrained environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_16369 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Prompt-based Dynamic Token Pruning for Efficient Segmentation of Medical Images Dutta, Pallabi Maity, Anubhab Mitra, Sushmita Computer Vision and Pattern Recognition The high computational demands of Vision Transformers (ViTs) in processing a large number of tokens often constrain their practical application in analyzing medical images. This research proposes a Prompt-driven Adaptive Token ({\it PrATo}) pruning method to selectively reduce the processing of irrelevant tokens in the segmentation pipeline. The prompt-based spatial prior helps to rank the tokens according to their relevance. Tokens with low-relevance scores are down-weighted, ensuring that only the relevant ones are propagated for processing across subsequent stages. This data-driven pruning strategy improves segmentation accuracy and inference speed by allocating computational resources to essential regions. The proposed framework is integrated with several state-of-the-art models to facilitate the elimination of irrelevant tokens, thereby enhancing computational efficiency while preserving segmentation accuracy. The experimental results show a reduction of $\sim$ 35-55% tokens; thus reducing the computational costs relative to baselines. Cost-effective medical image processing, using our framework, facilitates real-time diagnosis by expanding its applicability in resource-constrained environments. |
| title | Prompt-based Dynamic Token Pruning for Efficient Segmentation of Medical Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.16369 |