论文标题
将空间扩展嵌入整个扫描图像中的弱监督学习
Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images
论文作者
论文摘要
多个实例学习（MIL）是从WSI-Level注释中在Gigapixel全扫描图像（WSIS）上学习的广泛使用的框架。在大多数基于MIL的用于WSI级分析的分析管道中，WSI通常被分为贴片和深层特征（即贴片嵌入）在训练之前提取以降低整体计算成本，并应对GPU的有限RAM。为了克服这一限制，我们提出了启动器，即数据增强生成对抗网络（DA-GAN），可以合成嵌入空间中的数据增强，而不是在像素空间中，从而大大降低了计算要求。 SICAPV2数据集上的实验表明，我们的方法在不增加的情况下优于MIL，并且与传统的补丁级增强相提并论，用于MIL训练，同时更快。
Multiple Instance Learning (MIL) is a widely employed framework for learning on gigapixel whole-slide images (WSIs) from WSI-level annotations. In most MIL based analytical pipelines for WSI-level analysis, the WSIs are often divided into patches and deep features for patches (i.e., patch embeddings) are extracted prior to training to reduce the overall computational cost and cope with the GPUs' limited RAM. To overcome this limitation, we present EmbAugmenter, a data augmentation generative adversarial network (DA-GAN) that can synthesize data augmentations in the embedding space rather than in the pixel space, thereby significantly reducing the computational requirements. Experiments on the SICAPv2 dataset show that our approach outperforms MIL without augmentation and is on par with traditional patch-level augmentation for MIL training while being substantially faster.
