论文标题
DTFD-MIL：双层特征蒸馏多次实例学习组织病理学全幻灯片图像分类
DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification
论文作者
论文摘要
多个实例学习（MIL）越来越多地用于组织病理学整个幻灯片图像（WSIS）的分类。但是，对于此特定分类问题的MIL方法仍然面临着独特的挑战，尤其是与小样本队列有关的挑战。在其中，WSI幻灯片（袋）数量有限，而单个WSI的分辨率很大，这导致了从该幻灯片中裁剪的大量贴片（实例）。为了解决这个问题，我们建议通过介绍伪袋的概念来实际扩大行李的数量，在该概念上构建了双层MIL框架，以有效地使用固有功能。此外，我们还有助于在基于注意力的MIL的框架下得出实例概率，并利用该推导来帮助构建和分析提出的框架。所提出的方法的表现优于Camelyon-16上的其他最新方法，而在TCGA肺癌数据集中的性能也更好。提议的框架已准备好扩展到更广泛的MIL应用程序。该代码可在以下网址找到：https：//github.com/hrzhang1123/dtfd-mil
Multiple instance learning (MIL) has been increasingly used in the classification of histopathology whole slide images (WSIs). However, MIL approaches for this specific classification problem still face unique challenges, particularly those related to small sample cohorts. In these, there are limited number of WSI slides (bags), while the resolution of a single WSI is huge, which leads to a large number of patches (instances) cropped from this slide. To address this issue, we propose to virtually enlarge the number of bags by introducing the concept of pseudo-bags, on which a double-tier MIL framework is built to effectively use the intrinsic features. Besides, we also contribute to deriving the instance probability under the framework of attention-based MIL, and utilize the derivation to help construct and analyze the proposed framework. The proposed method outperforms other latest methods on the CAMELYON-16 by substantially large margins, and is also better in performance on the TCGA lung cancer dataset. The proposed framework is ready to be extended for wider MIL applications. The code is available at: https://github.com/hrzhang1123/DTFD-MIL
