通过拼接病理图像块提升癌症分类数据量，解决标注难问题。
Patch Stitching Data Augmentation for Cancer Classification in Pathology Images
- 用图像块拼接生成新病理图像，无需额外标注
- 在两个结直肠癌数据集上分类准确率提升
- 适合数据少、标注贵的医学图像研究者
计算病理学融合计算方法与数字成像，在疾病诊断与预后方面展现出巨大潜力。近年来，机器学习与深度学习的发展显著增强了计算病理学能力。然而，数据稀缺与数据不平衡问题仍对算法性能产生不利影响。本文提出一种高效且有效的数据增强策略，通过现有病理图像生成新图像，从而在不增加数据采集或标注成本的前提下扩充数据集。为评估该方法，我们在两组结直肠癌数据集上进行了实验，结果表明分类性能得到提升，说明该简单方法具有缓解计算病理学中数据稀缺与不平衡问题的潜力。
原文摘要 · Abstract (English)
Computational pathology, integrating computational methods and digital imaging, has shown to be effective in advancing disease diagnosis and prognosis. In recent years, the development of machine learning and deep learning has greatly bolstered the power of computational pathology. However, there still remains the issue of data scarcity and data imbalance, which can have an adversarial effect on any computational method. In this paper, we introduce an efficient and effective data augmentation strategy to generate new pathology images from the existing pathology images and thus enrich datasets without additional data collection or annotation costs. To evaluate the proposed method, we employed two sets of colorectal cancer datasets and obtained improved classification results, suggesting that the proposed simple approach holds the potential for alleviating the data scarcity and imbalance in computational pathology.
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