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New CHIS framework synthesizes histopathology images without annotation data

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了CHIS，一个旨在利用扩散模型增强组织病理学图像合成的新框架。该方法通过采用一个两阶段过程：结构初始化和纹理调制，绕过了对大量标注数据的需求。CHIS在生成过程中优化初始噪声状态并自适应地调整纹理，使在未标记图像上训练的扩散模型能够生成符合结构约束并保留组织风格的输出。实验表明，CHIS提高了生成保真度，并有利于下游分割任务。 AI

影响 这项研究可能会减少在医学影像中训练AI模型对专家标注的依赖，从而可能加速开发和部署。

排序理由 该集群包含一篇详细介绍新图像合成方法的学术论文。

在 arXiv cs.CV 阅读 →

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New CHIS framework synthesizes histopathology images without annotation data

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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Yuheng Qiu, Jingyi Luo, Chenfei Ye, Ting Ma, Jianfeng Cao ·

    可控组织病理学图像合成，采用无训练结构初始化和纹理调制

    arXiv:2606.27935v1 Announce Type: new Abstract: Deep learning has demonstrated remarkable success in high-throughput histopathology image analysis. However, the performance of learning-based models critically depends on the quality and size of annotations by expert pathologists, …

  2. arXiv cs.CV TIER_1 English(EN) · Jianfeng Cao ·

    可控组织病理学图像合成，采用无训练结构初始化和纹理调制

    Deep learning has demonstrated remarkable success in high-throughput histopathology image analysis. However, the performance of learning-based models critically depends on the quality and size of annotations by expert pathologists, which is a resource-intensive and time-consuming…

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