MambaMIL introduces a novel approach to Multiple Instance Learning (MIL) by incorporating the Selective Scan Space State Sequential Model (Mamba) for long sequence modeling. The Sequence Reordering Mamba (SR-Mamba) is proposed to understand order and distribution within sequences, improving discriminative feature extraction. Extensive experiments across diverse datasets demonstrate superior performance compared to state-of-the-art methods. The code is available on GitHub.
The digitalization of pathological images has led to computer-aided analysis in computational pathology, where MIL plays a crucial role. Existing MIL approaches face challenges in efficient interactions among instances and time-consuming computations. MambaMIL aims to overcome these limitations by integrating Mamba for long sequence modeling with linear complexity.
By utilizing SR-Mamba, MambaMIL can effectively capture more discriminative features from scattered positive patches within WSIs. The proposed framework outperforms existing methods on challenging tasks across multiple datasets, showcasing its effectiveness in computational pathology.
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arxiv.org
ข้อมูลเชิงลึกที่สำคัญจาก
by Shu Yang,Yih... ที่ arxiv.org 03-12-2024
https://arxiv.org/pdf/2403.06800.pdfสอบถามเพิ่มเติม