Ultrasound Image Lesion Detection Algorithm Optimized by Feature Feedback Mechanism
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摘要: 该文提出一种基于特征反馈机制的超声图像病灶检测方法，以实现超声病灶的实时精确定位与检测。所提方法由基于特征反馈机制的特征提取网络和基于分治策略的自适应检测头两部分组成。特征反馈网络通过反馈特征选取和加权融合计算，充分学习超声图像的全局上下文信息和局部低级语义细节以提高局部病灶特征的识别能力。自适应检测头对特征反馈网络所提取的多级特征进行分治预处理，通过将生理先验知识与特征卷积相结合的方式对各级特征分别进行病灶形状和尺度特征的自适应建模，增强检测头对不同大小病灶在多级特征下的检测效果。所提方法在甲状腺超声图像数据集上进行了测试，得到了70.3%的AP，99.0%的AP50和88.4%的AP75，实验结果表明，相较于主流检测算法，所提算法能实现更精准的实时超声图像病灶检测和定位。Abstract: A lesion detection method in ultrasound images based on feature feedback mechanism is proposed to realize real-time accurate localization and detection of ultrasound lesions. The proposed method consists of two parts: feature extraction network based on feature feedback mechanism and adaptive detection head based on divide-and-conquer strategy. The feature feedback network fully learns the global context information and local low-level semantic details of ultrasound images through feedback feature selection and weighted fusion calculation to improve the recognition ability of local lesion features. The adaptive detection head performs divide-and-conquer preprocessing on the multi-level features extracted by the feature feedback network. By combining physiological prior knowledge and feature convolution, adaptive modeling of lesion shape and scale features is performed on features at all levels to enhance the detection effect of the detection head on lesions of different sizes under multi-level features. The proposed method is tested on the thyroid ultrasound image dataset, and 70.3% AP, 99.0% AP50 and 88.4% AP75 are obtained. Experimental results show that the proposed algorithm can achieve more accurate real-time detection and positioning of ultrasound image lesions in comparison with mainstream detection algorithm.
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Key words:
- Lesion detection /
- Feature feedback /
- Adaptive detection head
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表 1 甲状腺超声病灶检测精度对比(%)
方法 骨干网 AP AP50 AP75 AP良性 AP恶性 Faster RCNN[7] ResNet50 64.3 96.6 79.2 61.5 67.1 RetinaNet[10] ResNet50 65.2 97.6 80.3 62.4 67.9 Yolov3[11] Darknet53 64.7 95.2 81.5 62.5 66.8 FCOS[12] ResNet50 65.8 95.5 80.8 63.5 68.2 EfficientDet[26] EfficientNet-B1 66.1 98.7 77.1 63.8 68.5 VarifocalNet[13] ResNet50 64.5 97.3 78.5 64.4 64.6 Yolof[14] ResNet50 65.9 99.2 81.4 64.8 66.9 Yolox[16] Darknet53 67.0 98.1 83.4 64.4 69.5 Yolov7[17] CBS+ELAN 67.3 98.3 84.0 65.3 69.2 DETR[20] ResNet50 63.4 93.6 76.2 61.2 65.7 DAB-DETR[21] ResNet50 64.9 96.3 78.9 64.1 65.8 DINO[22] ResNet50 66.1 95.8 83.6 62.5 69.7 本文 ResNet50 69.6 99.0 87.7 68.2 71.0 本文 ConvNeXt-tiny 70.3 99.0 88.4 68.9 71.6 
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表 2 病灶检测精度消融实验(%)
方法 AP AP50 AP75 Baseline 65.8 95.5 80.8 +ConvNeXt 67.5 98.7 84.8 +ConvNeXt+自适应检测头 68.5 98.6 86.8 +ConvNeXt+自适应检测头+特征反馈网络 70.3 99.0 88.4
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表 3 不同检测头对比(%)
方法 AP AP50 AP75 基线检测头(FCOS) 67.5 98.7 84.8 耦合检测头(Yolov3) 65.6 97.1 82.0 解耦合检测头(Yolox) 67.1 98.5 87.0 自适应检测头(本文) 68.5 98.6 86.8
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表 4 不同反馈方式对比
方法 AP(%) AP50(%) AP75(%) FPS(帧/s) 无反馈网络 68.5 98.6 86.8 46 $ {\boldsymbol{P}}_3^1 $至$ {\boldsymbol{P}}_5^1 $反馈网络+ASPP 69.6 98.6 87.4 40 $ {\boldsymbol{P}}_3^1 $至$ {\boldsymbol{P}}_7^1 $反馈网络+ASPP 69.6 98.4 87.8 34 $ {\boldsymbol{P}}_3^1 $至$ {\boldsymbol{P}}_5^1 $反馈网络+反馈特征
选取模块(本文)70.3 99.0 88.4 39 $ {\boldsymbol{P}}_3^1 $至$ {\boldsymbol{P}}_7^1 $反馈网络+反馈特征
选取模块70.1 98.5 88.2 30
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