부산대학교 도서관

Epithelium-stroma classification is always considered as an important preprocessing step for morphological quantitative analysis in image-based histological researches of oncologic diseases. However, large-scale accurate ground-truth labeling is expensive in histopathological image analysis, thus the classification performances will still be limited with the insufficient labeled training samples. Considering that acquisition of public unlabeled histopathological images is much cheaper, an epithelium-stroma classification framework is developed, based on the deep convolutional neural network framework and the strategies of self-taught learning. The method has the ability of taking advantage of large-scale unlabeled public histopathological data as auxiliary data, and then transferring the knowledge to enhance the performances in epithelium-stroma classification with limited labeled training data. The experiments demonstrate that the proposed method outperforms traditional CNNs when the labeled training data size is decreasing dramatically.