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 multisimilarity 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.

Texturebased Deep Learning for Effective Histopathological Cancer Image Classification Automatic histopathological Whole Slide Image (WSI) analysis for cancer classification has been highlighted along with the advancements in microscopic imaging techniques, since manual examination and diagnosis with WSIs are time and costconsuming. Recently, deep convolutional neural networks have succeeded in histopathological image analysis. However, despite the success of the development, there are still opportunities for further enhancements. In this paper, we propose a novel cancer texturebased deep neural network (CATNet) that learns scalable morphological features from histopathological WSIs. The innovation of CATNet is twofold: (1) capturing invariant spatial patterns by dilated convolutional layers and (2) improving predictive performance while reducing model complexity. Moreover, CATNet can provide discriminative morphological (texture) patterns formed on cancerous regions of histopathological images comparing to normal regions. We elucidated how our proposed method, CATNet, captures morphological patterns of interest in hierarchical levels in the model. The proposed method outperformed the current stateoftheart benchmark methods on accuracy, precision, recall, and F1 score.