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English(EN) LEAF-Mamba: Feature Adaptation and Fusion for Whole Slide Image Analysis

LEAF-Mamba框架增强了用于诊断的全切片图像分析

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了LEAF-Mamba，一个旨在改进全切片图像（WSIs）计算诊断和预后分析的新型框架。该方法通过采用多实例学习来应对WSIs的千兆像素大小和有限标注带来的挑战。LEAF-Mamba集成了用于任务特定表示学习的重构正则化适配器，并利用并行的Mamba和卷积流来捕捉长距离和局部依赖关系。实验表明，LEAF-Mamba在诊断分类和生存预测任务上优于现有方法。 AI

影响 通过先进的特征适应和融合技术，增强了医学影像中的计算诊断和预后能力。

排序理由 这是一篇详细介绍新图像分析框架的研究论文。

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

LEAF-Mamba框架增强了用于诊断的全切片图像分析

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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Lubin Gan, Jing Zhang, Mingjun Ma, Linhao Qu, Dachun Kai, Xiaoyan Sun ·

    LEAF-Mamba：全切片图像分析的特征自适应与融合

    arXiv:2610.29746v1 Announce Type: new Abstract: Whole slide image (WSI) analysis enables computational diagnosis and prognosis, but gigapixel images and limited annotations make slide-level learning challenging. Multiple instance learning addresses this task by aggregating patch …

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