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English(EN) CGRL: Concept-Guided Pruning and Representation Learning for Whole-Slide Image Classification

新的CGRL框架增强了病理学中的全切片图像分类

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一个名为CGRL的新框架，用于改进计算病理学中的全切片图像分类。该方法使用从疾病提示派生的类别级概念原型来指导过程。CGRL首先通过对其与概念的相似性进行排名来修剪图像块，仅保留最相关的图像块进行分析。然后，它采用概念引导的对比表示学习来优化图像块嵌入，从而提高准确性并降低计算成本。 AI

影响 这项研究通过改进医学图像的分析，有望在计算病理学中带来更准确、更高效的诊断工具。

排序理由 该集群包含一篇详细介绍图像分类新方法的学术论文。

在 arXiv cs.CV 阅读 →

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新的CGRL框架增强了病理学中的全切片图像分类

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该集群包含一篇详细介绍图像分类新方法的学术论文。
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Thuc Huynh, Tuan Le, Doanh C. Bui ·

    CGRL：用于全切片图像分类的概念引导剪枝与表示学习

    arXiv:2607.12556v1 Announce Type: new Abstract: Weakly supervised whole-slide image (WSI) classification is widely used in computational pathology because slide-level labels are easier to obtain than dense region annotations. Existing multiple instance learning (MIL) methods ofte…

  2. arXiv cs.CV TIER_1 English(EN) · Doanh C. Bui ·

    CGRL：用于全切片图像分类的概念引导剪枝与表示学习

    Weakly supervised whole-slide image (WSI) classification is widely used in computational pathology because slide-level labels are easier to obtain than dense region annotations. Existing multiple instance learning (MIL) methods often aggregate large bags of patch embeddings using…

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