Title: Compact and De-biased Negative Instance Embedding for Multi-Instance Learning on Whole-Slide Image Classification Show Chinese title

Title: 多实例学习的紧凑且去偏负样本嵌入用于全幻灯片图像分类

Abstract: Whole-slide image (WSI) classification is a challenging task because 1) patches from WSI lack annotation, and 2) WSI possesses unnecessary variability, e.g., stain protocol. Recently, Multiple-Instance Learning (MIL) has made significant progress, allowing for classification based on slide-level, rather than patch-level, annotations. However, existing MIL methods ignore that all patches from normal slides are normal. Using this free annotation, we introduce a semi-supervision signal to de-bias the inter-slide variability and to capture the common factors of variation within normal patches. Because our method is orthogonal to the MIL algorithm, we evaluate our method on top of the recently proposed MIL algorithms and also compare the performance with other semi-supervised approaches. We evaluate our method on two public WSI datasets including Camelyon-16 and TCGA lung cancer and demonstrate that our approach significantly improves the predictive performance of existing MIL algorithms and outperforms other semi-supervised algorithms. We release our code at https://github.com/AITRICS/pathology_mil. Show Chinese abstract

Abstract: 全切片图像（WSI）分类是一项具有挑战性的任务，因为1）来自WSI的图像块缺乏注释，且2）WSI具有不必要的变异性，例如染色协议。 最近，多实例学习（MIL）取得了显著进展，使得可以基于切片级别的注释而不是图像块级别的注释进行分类。 然而，现有的MIL方法忽略了正常切片的所有图像块都是正常的这一事实。 利用这一免费注释，我们引入了一个半监督信号，以消除切片间的变异性并捕捉正常图像块内的共同变化因素。 由于我们的方法与MIL算法是正交的，我们在最近提出的MIL算法基础上评估了我们的方法，并与其他半监督方法进行了性能比较。 我们在两个公开的WSI数据集上进行了评估，包括Camelyon-16和TCGA肺癌数据集，并证明我们的方法显著提高了现有MIL算法的预测性能，并优于其他半监督算法。 我们将在https://github.com/AITRICS/pathology_mil发布我们的代码。