SSE: Scale-adaptive Soft Erase Weakly Supervised Segmentation Network for Thyroid Ultrasound Images

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.