To distinguish ambiguous images during specimen slides viewing, pathologists usually spend lots of time to seek guidance from confirmed similar images or cases, which is inefficient. Therefore, several histopathological image retrieval methods have been proposed for pathologists to easily obtain images sharing similar content with the query images. However, these methods cannot ensure a reasonable similarity metric, and some of them need lots of annotated images to train a feature extractor to represent images. Motivated by this circumstance, we propose the first deep metric learning-based histopathological image retrieval method in this paper and construct a deep neural network based on the mixed attention mechanism to learn an embedding function under the supervision of image category information. With the learned embedding function, original images are mapped into the predefined metric space where similar images from the same category are close to each other, so that the distance between image pairs in the metric space can be regarded as a reasonable metric for image similarity. We evaluate the proposed method on two histopathological image retrieval datasets: our self-established dataset and a public dataset called Kimia Path24, on which the proposed method achieves recall in top-1 recommendation (Recall@1) of 84.04% and 97.89% respectively. Moreover, further experiments confirm that the proposed method can achieve comparable performance to several published methods with less training data, which hedges the shortage of annotated medical image data to some extent. Code is available at https://github.com/easonyang1996/DML_HistoImgRetrieval.

中文翻译：

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一种用于组织病理学图像检索的深度度量学习方法

病理学家为了在标本切片查看过程中区分模糊图像，通常会花费大量时间从已确认的相似图像或病例中寻求指导，这是低效的。因此，已经提出了几种组织病理学图像检索方法供病理学家轻松获取与查询图像共享相似内容的图像。然而，这些方法不能确保合理的相似度度量，其中一些方法需要大量带注释的图像来训练特征提取器来表示图像。受此启发，我们在本文中提出了第一个基于深度度量学习的组织病理学图像检索方法，并构建了基于混合注意机制的深度神经网络，以在图像类别信息的监督下学习嵌入函数。使用学习的嵌入函数，将原始图像映射到预定义的度量空间，其中来自同一类别的相似图像彼此接近，这样度量空间中的图像对之间的距离可以被视为图像相似性的合理度量。我们在两个组织病理学图像检索数据集上评估了所提出的方法：我们自建的数据集和名为 Kimia Path24 的公共数据集，所提出的方法在 top-1 推荐（Recall@1）中的召回率分别为 84.04% 和 97.89%。此外，进一步的实验证实，所提出的方法可以在训练数据较少的情况下实现与几种已发表方法相当的性能，这在一定程度上弥补了带注释的医学图像数据的不足。代码可在 https://github.com/easonyang1996/DML_HistoImgRetrieval 获得。