Abstract
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.
J. Wu and J.-X. Zhong—These authors contributed equally.
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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
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DOI: https://doi.org/10.1007/978-3-030-35817-4_14
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