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In Artificial intelligence in medicine ; h5-index 34.0

Deep learning on a limited number of labels/annotations is a challenging task for medical imaging analysis. In this paper, we propose a novel self-training segmentation pipeline (Self-Seg in short) for segmenting skeletal muscle in CT images. Self-Seg starts with a small set of annotated images and then iteratively learns from unlabeled datasets to gradually improve the segmentation performance. Self-Seg follows a semi-supervised teacher-student learning scheme and there are two contributions: 1) we construct a self-attention UNet to improve segmentation over the classical UNet model, and 2) we implement an automatic label grader to implicitly incorporate medical knowledge for quality assurance of pseudo labels, from which good quality pseudo labels are identified to enhance learning of the segmentation model. We perform extensive experiments on three CT image datasets and show promising results on five evaluation settings, and we also compared our method to several baseline and related methods and achieved superior performance.

Hao Degan, Ahsan Maaz, Salim Tariq, Duarte-Rojo Andres, Esmaeel Dadashzadeh, Zhang Yudong, Arefan Dooman, Wu Shandong


Image segmentation, Self-attention, Semi-supervised learning, Skeletal muscle, Teacher-student model