Interaction-Oriented Feature Decomposition for Medical Image Lesion Detection
Interaction-Oriented Feature Decomposition for Medical Image Lesion Detection
复制标题
用于医学图像病变检测的面向交互的特征分解
DOI：
10.1007/978-3-031-16437-8_31
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Jiang Liu
中科院分区：
文献类型：
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作者：
Junyong Shen;Yan Hu;Xiaoqin Zhang;Zhongxi Qiu;Tingming Deng;Yanwu Xu;Jiang Liu
常见的病变检测网络通常使用病变特征进行分类和定位。然而，许多病变仅根据病变特征进行分类，而没有考虑与全局上下文特征的关系，这引起了误分类问题。在本文中，我们提出了一个面向交互的特征分解（IOFD）网络，以提高检测性能的上下文相关的病变。具体来说，我们分解的功能输出从骨干到全局上下文功能和病变功能，独立优化。然后，我们设计了两个新的模块，以提高病变分类的准确性。设计了全局上下文嵌入（GCE）模块提取全局上下文特征。全局上下文交叉注意（GCCA）模块没有额外的参数被设计用于建模全局上下文特征和病变特征之间的相互作用。此外，考虑到分类和定位任务所需的不同特征，我们进一步采用了任务解耦策略。IOFD在训练和推理方面易于训练和端到端。在两种模式下的数据集上的实验结果优于现有算法，证明了IOFD的有效性和通用性。源代码可在https://github.com/mklz-sjy/IOFD上获取
Common lesion detection networks typically use lesion features for classification and localization. However, many lesions are classified only by lesion features without considering the relation with global context features, which raises the misclassification problem. In this paper, we propose an Interaction-Oriented Feature Decomposition (IOFD) network to improve the detection performance on context-dependent lesions. Specifically, we decompose features output from a backbone into global context features and lesion features that are optimized independently. Then, we design two novel modules to improve the lesion classification accuracy. A Global Context Embedding (GCE) module is designed to extract global context features. A Global Context Cross Attention (GCCA) module without additional parameters is designed to model the interaction between global context features and lesion features. Besides, considering the different features required by classification and localization tasks, we further adopt a task decoupling strategy. IOFD is easy to train and end-to-end in terms of training and inference. The experimental results for datasets in two modalities outperform state-of-the-art algorithms, which demonstrates the effectiveness and generality of IOFD. The source code is available at https://github.com/mklz-sjy/IOFD