Graph Learning in Medical Imaging

Book Title

Graph Learning in Medical Imaging

Chapter Title

Weakly- and Semi-supervised Graph CNN for Identifying Basal Cell Carcinoma on Pathological Images

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Editors

Daoqiang Zhang, Luping Zhou, Biao Jie, Mingxia Liu

Description

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.

First Page

112

Last Page

119

ISBN

978-3-030-35817-4

Publication Date

11-14-2019

Publisher

Springer

City

Switzerland

Keywords

Basal Cell Carcinoma, Graph-CNN, Pathology

Disciplines

Pathology

Graph Learning in Medical Imaging

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