Accurate detection of thyroid lesions is a critical aspect of computer-aided diagnosis. However, most existing detection methods perform only one feature extraction process and then fuse multi-scale features, which can be affected by noise and blurred features in ultrasound images. In this study, we propose a novel detection network based on a feature feedback mechanism inspired by clinical diagnosis. The mechanism involves first roughly observing the overall picture and then focusing on the details of interest. It comprises two parts: a feedback feature selection module and a feature feedback pyramid. The feedback feature selection module efficiently selects the features extracted in the first phase in both space and channel dimensions to generate high semantic prior knowledge, which is similar to coarse observation. The feature feedback pyramid then uses this high semantic prior knowledge to enhance feature extraction in the second phase and adaptively fuses the two features, similar to fine observation. Additionally, since radiologists often focus on the shape and size of lesions for diagnosis, we propose an adaptive detection head strategy to aggregate multi-scale features. Our proposed method achieves an AP of 70.3% and AP50 of 99.0% on the thyroid ultrasound dataset and meets the real-time requirement. The code is available at https://github.com/HIT-wanglingtao/Thinking-Twice.

翻译：甲状腺病灶的精准检测是计算机辅助诊断的关键环节。然而，现有检测方法大多仅执行一次特征提取过程，随后对多尺度特征进行融合，这容易受到超声图像中噪声和模糊特征的影响。本研究受临床诊断启发性提出了一种基于特征反馈机制的新型检测网络。该机制遵循先粗览全局、再关注细节的临床诊断思路，由两个部分组成：反馈特征选择模块和特征反馈金字塔。反馈特征选择模块在空间和通道维度高效筛选第一阶段提取的特征，生成高语义先验知识——这类似于粗观察过程；随后特征反馈金字塔利用该高语义先验知识强化第二阶段特征提取，并自适应融合两阶段特征——这类似于细观察过程。此外，鉴于放射科医生常依据病灶形态和尺寸进行诊断，我们提出一种自适应检测头策略以聚合多尺度特征。所提方法在甲状腺超声数据集上实现了70.3%的AP和99.0%的AP50，同时满足实时检测需求。相关代码已开源至 https://github.com/HIT-wanglingtao/Thinking-Twice。