Abstract
Multiple instance learning (MIL) has emerged as a popular method for classifying histopathology whole slide images (WSIs). However, existing approaches typically rely on pre-trained models from large natural image datasets, such as ImageNet, to generate instance features, which can be sub-optimal due to the significant differences between natural images and histopathology images that lead to a domain shift. In this paper, we present a novel, simple yet effective method for learning domain-specific knowledge transformation from pre-trained models to histopathology images. Our approach entails using a prompt component to assist the pre-trained model in discerning differences between the pre-trained dataset and the target histopathology dataset, resulting in improved performance of MIL models. We validate our method on two publicly available datasets, Camelyon16 and TCGA-NSCLC. Extensive experimental results demonstrate the significant performance improvement of our method for different MIL models and backbones. Upon publication of this paper, we will release the source code for our method.
Abstract (translated)
多个实例学习(MIL)已经成为一种分类病理全 slide 图像(WSIs)的流行方法。然而，现有的方法通常依赖于从大型自然图像数据集(如 ImageNet)训练的模型生成实例特征，这些特征可能不如最佳水平，因为自然图像和病理图像之间存在显著的差异，导致域转换。在本文中，我们提出了一种新颖、简单但有效的方法，用于从训练模型到病理图像的知识转型学习。我们的方法是使用一个触发器组件来帮助训练模型区分训练数据和目标病理数据集之间的差异，从而改进 MIL 模型的性能。我们了两个公开数据集进行了验证，分别是Camelyon16和TCGA-NSCLC。广泛的实验结果证明了我们方法对不同 MIL 模型和骨架的显著性能改进。在本文发表后，我们将发布我们方法的源代码。
URL
https://arxiv.org/abs/2303.13122
