Histo-DD
Histo-DD是针对组织病理学图像的数据集蒸馏算法，通过生成合成样本减少训练成本，提升分类效率。
基本信息
资源简介
Histo-DD是一个针对组织病理学图像的数据集蒸馏算法，由澳大利亚健康创新研究所开发。该算法通过整合染色标准化和模型增强技术，将大型数据集压缩成一组合成样本，以提高训练效率和简化下游应用。主要应用于组织病理学图像的分类任务，通过生成合成样本减少训练所需的大量补丁，同时保留区分性信息，显著降低训练成本。
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使用方式
数据集说明
Histo-DD 对应论文数据集（arXiv 预印本）。
数据获取指引
- 打开论文页面获取作者与项目信息：https://arxiv.org/abs/2408.09709v1
- 论文 Data Availability / Code Availability 章节标注了数据实际托管位置；
- 获取到实际数据链接后，按对应平台标准方式下载。
论文摘要：Abstract:Deep neural networks (DNNs) have exhibited remarkable success in the field of histopathology image analysis. On the other hand, the contemporary trend of employing large models and extensive datasets has underscored the significance of dataset distillation, which involves compressing large-scale datasets into a condensed set of synthetic samples, offering distinct advantages in improving training efficiency and streamlining downstream applications. In this work, we introduce a novel dataset distillation algorithm tailored for histopathology image datasets (Histo-DD), which integrates stain normalisation and model augmentation into the distillation progress. Such integration can substantially enhance the compatibility with histopathology images that are often characterised by high colour heterogeneity. We conduct a comprehensive evaluation of the effectiveness of the proposed algorithm and the generated histopathology samples in both patch-level and slide-level classification tasks. The experimental results, carried out on three publicly available WSI datasets, including Camelyon16, TCGA-IDH, and UniToPath, demonstrate that the proposed Histo-DD can generate more informative synthetic patches than previous coreset selection and patch sampling methods. Moreover, the synthetic samples can preserve discriminative information, substantially reduce training efforts, and exhibit architecture-agnostic properties. These advantages indicate that synthetic samples can serve as an alternative to large-scale datasets.
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