From Pixels to Gigapixels: Bridging Local Inductive Bias and Long-Range Dependencies with Pixel-Mamba

• URL: http://arxiv.org/abs/2412.16711v1
• Date: Sat, 21 Dec 2024 17:45:27 GMT
• Title: From Pixels to Gigapixels: Bridging Local Inductive Bias and Long-Range Dependencies with Pixel-Mamba
• Authors: Zhongwei Qiu, Hanqing Chao, Tiancheng Lin, Wanxing Chang, Zijiang Yang, Wenpei Jiao, Yixuan Shen, Yunshuo Zhang, Yelin Yang, Wenbin Liu, Hui Jiang, Yun Bian, Ke Yan, Dakai Jin, Le Lu,
• Abstract summary: We introduce Pixel-Mamba, a novel deep learning architecture designed to efficiently handle gigapixel WSIs. Pixel-Mamba incorporates local inductive biases through progressively expanding tokens, akin to convolutional neural networks. Experiments demonstrate the efficacy of Pixel-Mamba as a powerful and efficient framework for end-to-end WSI analysis.
• Score: 16.19372261506739
• License:
• Abstract: 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.

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