当前位置:网站首页>[论文阅读] TUN-Det: A Novel Network for Thyroid Ultrasound Nodule Detection
[论文阅读] TUN-Det: A Novel Network for Thyroid Ultrasound Nodule Detection
2022-07-04 23:48:00 【xiongxyowo】
Abstract
本文提出了一种新型的单阶段检测模型TUN-Det,用于超声扫描的甲状腺结节检测。主要贡献是:(i)引入Residual U-blocks(RSU)来构建我们的TUN-Det的主干;(ii)新设计的多头架构,由三个平行的RSU变体组成,以取代分类和回归头的普通卷积层。剩余块使骨干网的每个阶段都能提取局部和全局特征,这在检测不同大小和外观的结节中发挥了重要作用。多头设计将集合策略嵌入到一个端到端的模块中,通过融合不同子模块产生的多个输出来提高准确性和稳健性。在700名患者的1268个甲状腺结节上进行的实验结果表明,我们新提出的RSU主干和用于分类和回归头的多头结构大大改善了对基线模型的检测精度。我们的TUN-Det在总体平均精度(AP)指标上也取得了与最先进的模型相比非常有竞争力的结果,并在 A P 35 AP_{35} AP35和 A P 50 AP_{50} AP50方面优于它们,这表明它在临床应用中具有良好的性能。
Method
本文属于一项目标检测任务,以从超声图像中检测甲状腺结节。核心思想是使用一种更高级的Encoder Block(Residual U-blocks)来提取多尺度的特征,如下所示:
可以看到,本文的一大特点是没有使用任何预训练的backbone(如resnet)之类的,而是使用了文中提出的Residual U-blocks(RSU)。而这个RSU的话其实就是一个小型的UNet,以在Encoder本身提升多层次的特征提取能力。当然,文中的分类头与回归头也换成了multi-layer的形式,如下:
实现形式有点类似于分割任务中的deep supervision,即综合不同层次的输出进行监督,而非仅监督最终结果。
总的来讲是将U^2 Net[1]的思想从显著性检测领域迁移到目标检测领域,事实上这两篇文章的作者就是同一人。
Ref
[1] U2-Net: Going deeper with nested U-structure for salient object detection
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