Multi‐scale consistent self‐training network for semi‐supervised orbital tumor segmentation
Keyi Wang,
Kai Jin,
Zhiming Cheng
et al.
Abstract:PurposeSegmentation of orbital tumors in CT images is of great significance for orbital tumor diagnosis, which is one of the most prevalent diseases of the eye. However, the large variety of tumor sizes and shapes makes the segmentation task very challenging, especially when the available annotation data is limited.MethodsTo this end, in this paper, we propose a multi‐scale consistent self‐training network (MSCINet) for semi‐supervised orbital tumor segmentation. Specifically, we exploit the semantic‐invarianc… Show more
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