Graph Learning in Medical Imaging
Daoqiang Zhang, Luping Zhou, Biao Jie, Mingxia Liu
Deep learning has been used to identify Basal Cell Carcinoma (BCC) from pathology images. The traditional patch-based strategy has the problem of integrating patch level information into the whole image level prediction. Also, it is often difficult to obtain sufficient high-quality patch labels such as pixel-wise segmentation masks. Benefiting from the recent development of Graph-CNN (GCN), we propose a new weakly- and semi-supervised GCN architecture to model patch-patch relation and provide patch-aware interpretability. Integrating prior knowledge and structure information, without relying on pixel-wise segmentation labels, our whole image level prediction achieves state-of-art performance with mAP 0.9556 and AUC 0.9502. Further visualization demonstrates that our model is implicitly consistent with the pixel-wise segmentation labels, which indicates our model can identify the region of interests without relying on the pixel-wise labels.
Basal Cell Carcinoma, Graph-CNN, Pathology
Wu J., Zhong JX., Chen E.Z., Zhang J., Ye J.J., Yu L. (2019) Weakly- and Semi-supervised Graph CNN for Identifying Basal Cell Carcinoma on Pathological Images. In: Zhang D., Zhou L., Jie B., Liu M. (eds) Graph Learning in Medical Imaging. GLMI 2019. Lecture Notes in Computer Science, vol 11849. Springer, Cham. https://doi.org/10.1007/978-3-030-35817-4_14