Patch-level Tumor Classification in Digital Histopathology Images with Domain Adapted Deep Learning.

Tumor histopathology is a crucial step in cancer diagnosis which involves visual inspection of imaging data to detect the presence of tumor cells among healthy tissues. This manual process can be time-consuming, error-prone, and influenced by the expertise of the pathologist. Recent deep learning methods for image classification and detection using convolutional neural networks (CNNs) have demonstrated marked improvements in the accuracy of a variety of medical imaging analysis tasks. However, most well-established deep learning methods require large annotated training datasets that are specific to the particular problem domain; such datasets are difficult to acquire for histopathology data where visual characteristics differ between different tissue types, in addition to the need for precise annotations. In this study, we overcome the lack of annotated training dataset in histopathology images of a particular domain by adapting annotated histopathology images from different domains (tissue types). The data from other tissue types are used to pre-train CNNs into a shared histopathology domain (e.g., stains, cellular structures) such that it can be further tuned/optimized for a specific tissue type. We evaluated our classification method on publically available datasets of histopathology images; the accuracy and area under the receiver operating characteristic curve (AUC) of our method was higher than CNNs trained from scratch on limited data (accuracy: 84.3% vs. 78.3%; AUC: 0.918 vs. 0.867), suggesting that domain adaptation can be a valuable approach to histopathological images classification.