Feasibility of fully automated classification of whole slide images based on deep learning

Although microscopic analysis of tissue slides has been the basis for disease diagnosis for decades, intra- and inter-observer variabilities remain issues to be resolved. The recent introduction of digital scanners has allowed for using deep learning in the analysis of tissue images because many whole slide images (WSIs) are accessible to researchers. In the present study, we investigated the possibility of a deep learning-based, fully automated, computer-aided diagnosis system with WSIs from a stomach adenocarcinoma dataset. Three different convolutional neural network architectures were tested to determine the better architecture for tissue classifier. Each network was trained to classify small tissue patches into normal or tumor. Based on the patch-level classification, tumor probability heatmaps can be overlaid on tissue images. We observed three different tissue patterns, including clear normal, clear tumor and ambiguous cases. We suggest that longer inspection time can be assigned to ambiguous cases compared to clear normal cases, increasing the accuracy and efficiency of histopathologic diagnosis by pre-evaluating the status of the WSIs. When the classifier was tested with completely different WSI dataset, the performance was not optimal because of the different tissue preparation quality. By including a small amount of data from the new dataset for training, the performance for the new dataset was much enhanced. These results indicated that WSI dataset should include tissues prepared from many different preparation conditions to construct a generalized tissue classifier. Thus, multi-national/multi-center dataset should be built for the application of deep learning in the real world medical practice.

Application perspective of large pathological section coupled with artificial intelligence in the diagnosis of pancreatic tumors / 中华胰腺病杂志

Large pathological sections can display tumors and the para-tumor tissues holistically and completely on one slice, which is beneficial to the whole observation and evaluation of tumors and their surrounding microenvironments, thus gaining a comprehensive understanding about the disease. With the gradual realization of digital pathology and advancement in computational pathology, artificial intelligence has made it possible to achieve accurate and individualized diagnosis and treatment of pancreatic tumors by linking up morphologies of different tumor cells and surrounding microenvironments with various types of data including image omics, gene proteomics and clinical data, which is both an opportunity and a challenge for Chinese researchers.