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| Hauptverfasser: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2412.16711 |
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| _version_ | 1866929644363579392 |
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| author | Qiu, Zhongwei Chao, Hanqing Lin, Tiancheng Chang, Wanxing Yang, Zijiang Jiao, Wenpei Shen, Yixuan Zhang, Yunshuo Yang, Yelin Liu, Wenbin Jiang, Hui Bian, Yun Yan, Ke Jin, Dakai Lu, Le |
| author_facet | Qiu, Zhongwei Chao, Hanqing Lin, Tiancheng Chang, Wanxing Yang, Zijiang Jiao, Wenpei Shen, Yixuan Zhang, Yunshuo Yang, Yelin Liu, Wenbin Jiang, Hui Bian, Yun Yan, Ke Jin, Dakai Lu, Le |
| contents | Histopathology plays a critical role in medical diagnostics, with whole slide images (WSIs) offering valuable insights that directly influence clinical decision-making. However, the large size and complexity of WSIs may pose significant challenges for deep learning models, in both computational efficiency and effective representation learning. In this work, we introduce Pixel-Mamba, a novel deep learning architecture designed to efficiently handle gigapixel WSIs. Pixel-Mamba leverages the Mamba module, a state-space model (SSM) with linear memory complexity, and incorporates local inductive biases through progressively expanding tokens, akin to convolutional neural networks. This enables Pixel-Mamba to hierarchically combine both local and global information while efficiently addressing computational challenges. Remarkably, Pixel-Mamba achieves or even surpasses the quantitative performance of state-of-the-art (SOTA) foundation models that were pretrained on millions of WSIs or WSI-text pairs, in a range of tumor staging and survival analysis tasks, {\bf even without requiring any pathology-specific pretraining}. Extensive experiments demonstrate the efficacy of Pixel-Mamba as a powerful and efficient framework for end-to-end WSI analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_16711 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From Pixels to Gigapixels: Bridging Local Inductive Bias and Long-Range Dependencies with Pixel-Mamba Qiu, Zhongwei Chao, Hanqing Lin, Tiancheng Chang, Wanxing Yang, Zijiang Jiao, Wenpei Shen, Yixuan Zhang, Yunshuo Yang, Yelin Liu, Wenbin Jiang, Hui Bian, Yun Yan, Ke Jin, Dakai Lu, Le Computer Vision and Pattern Recognition Histopathology plays a critical role in medical diagnostics, with whole slide images (WSIs) offering valuable insights that directly influence clinical decision-making. However, the large size and complexity of WSIs may pose significant challenges for deep learning models, in both computational efficiency and effective representation learning. In this work, we introduce Pixel-Mamba, a novel deep learning architecture designed to efficiently handle gigapixel WSIs. Pixel-Mamba leverages the Mamba module, a state-space model (SSM) with linear memory complexity, and incorporates local inductive biases through progressively expanding tokens, akin to convolutional neural networks. This enables Pixel-Mamba to hierarchically combine both local and global information while efficiently addressing computational challenges. Remarkably, Pixel-Mamba achieves or even surpasses the quantitative performance of state-of-the-art (SOTA) foundation models that were pretrained on millions of WSIs or WSI-text pairs, in a range of tumor staging and survival analysis tasks, {\bf even without requiring any pathology-specific pretraining}. Extensive experiments demonstrate the efficacy of Pixel-Mamba as a powerful and efficient framework for end-to-end WSI analysis. |
| title | From Pixels to Gigapixels: Bridging Local Inductive Bias and Long-Range Dependencies with Pixel-Mamba |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.16711 |