Liver Histopathological Image Retrieval Based on Deep Metric Learning

Histopathological image retrieval aims to search histopathological images sharing similar content with the query image, which could provide pathologists with an approach to easily obtain similar diagnostic cases for reference. Recent histopathological image retrieval methods are usually based on CNN feature extractors, which require a large amount of annotated data for training. Besides, most of existing methods could not define a reasonable similarity metric for histopathological images. In this paper, we apply deep metric learning to liver histopathological image retrieval. We construct a model based on mixed attention mechanism and train the model with a modified version of multi-similarity loss, which enables embedding vectors of similar images in the given metric space to be closer and dissimilar ones to be far from each other. Additionally, our model can be well fitted with limited data. Finally, we evaluate the proposed method with our own established liver histopathological image dataset. Compared with several published methods, our model shows higher performance.