用于全切片图像分类多示例学习的紧凑且去偏的负示例嵌入
Compact and De-biased Negative Instance Embedding for Multi-Instance Learning on Whole-Slide Image Classification
AI总结：
针对WSI分类中图像块缺乏标注及切片间变异性问题，提出一种与MIL算法正交的半监督去偏方法，利用正常切片的免费标注显著提升了现有MIL算法的预测性能。
摘要：
全切片图像（WSI）分类是一项具有挑战性的任务，原因在于：1）来自WSI的图像块缺乏标注；2）WSI存在不必要的变异性，例如染色协议。近期，多示例学习（MIL）取得了显著进展，允许基于切片级而非图像块级的标注进行分类。然而，现有的MIL方法忽略了来自正常切片的所有图像块均为正常这一事实。利用这一免费标注，我们引入了一种半监督信号，以对切片间的变异性进行去偏，并捕捉正常图像块内的共同变异因子。由于我们的方法与MIL算法正交，我们在近期提出的MIL算法之上评估了该方法，并与其他半监督方法的性能进行了比较。我们在包括Camelyon-16和TCGA肺癌在内的两个公开WSI数据集上评估了该方法，结果表明我们的方法显著提升了现有MIL算法的预测性能，并优于其他半监督算法。我们在https://github.com/AITRICS/pathology_mil发布了代码。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
