分割
计算机科学
人工智能
雅卡索引
模式识别(心理学)
图像分割
Sørensen–骰子系数
特征(语言学)
尺度空间分割
计算机视觉
作者
Mei Yu,Ming Han,Xuewei Li,Xi Wei,Jialin Zhu,Han Jiang,Zhiqiang Liu,Ruixaun Zhang,Ruiguo Yu
标识
DOI:10.1109/bibm52615.2021.9669589
摘要
Weakly supervised segmentation techniques based on medical images ease the reliance of models on pixel-level annotation while advancing the field of computer-aided diagnosis. However, the differences in nodule size in thyroid ultrasound images and the limitations of class activation maps in weakly supervised segmentation methods lead to under- and over-segmentation problems in prediction. To alleviate this problem, we propose a novel weakly supervised segmentation network. This method is based on a dual branch soft erase module that expands the foreground response region while constraining the erroneous expansion of the foreground region by the enhancement of background features. In addition, the sensitivity of the network to the nodule scale size is enhanced by the scale feature adaptation module, which in turn generates integral and high-quality segmentation masks. The results of experiments performed on the thyroid ultrasound image dataset showed that our model outperformed existing weakly supervised semantic segmentation methods with Jaccard and Dice coefficients of 50.1% and 64.5%, respectively.
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