基于注意力机制的甲状腺超声图像感兴趣区域定位方法

Location of regions of interest in thyroid ultrasound images based on attention mechanism

针对甲状腺超声图像中背景干扰及数据集规模受限的问题,提出了基于注意力机制的甲状腺超声图像感兴趣区域定位方法。采用跨尺度注意力交互策略,改进定位模型的特征网络,提高不同尺度下各层级特征的融合效率;通过知识蒸馏实现特征网络特征提取能力的强化,解决数据规模不足引起的网络过拟合问题;依据解剖学甲状腺形态统计分布设计t掩码,联合注意力掩码计算特征损失,引导网络对甲状腺超声图像关键通道和像素信息的学习,实现对甲状腺超声图像感兴趣区域的定位。实验结果表明,当IoU阈值为0.5时,甲状腺超声图像感兴趣区域定位AP达到92.7%,对辅助医生进行甲状腺疾病的诊断具有临床意义和价值。

To address the problems of background interference and limited dataset size,we propose a method for locating the region of interest in thyroid ultrasound images.The method utilizes an attention mechanism based on cross-scale attention interaction strategy to improve the fusion efficiency of hierarchical features in the localization model.The feature network of the localization model is enhanced through knowledge distillation to solve the problem of overfitting.A t-mask is designed based on the statistical distribution of anatomical thyroid morphology,and a joint attention mask is calculated to guide the network in learning key channels and pixel information of thyroid ultrasound images,thereby achieving the localization of the region of interest.Experimental results demonstrate that the average precision(AP)for thyroid ultrasound image region of interest localization reaches 92.7%when the IoU threshold is set to 0.5,which is clinically significant and valuable for assisting doctors in diagnosing thyroid diseases.