论文标题
基于无锚结构的数字乳腺摄影中的乳房质量检测
Breast mass detection in digital mammography based on anchor-free architecture
论文作者
论文摘要
背景和客观：乳房X线摄影图中乳腺肿块的准确检测对于诊断早期乳腺癌至关重要，这可以大大提高患者的存活率。但是，由于乳房肿块的异质性和周围环境的复杂性。我们引入了一种截断归一化方法，并将其与自适应直方图均衡相结合，以增强乳房质量和周围环境之间的对比度。同时，为了解决由小数据大小引起的过度拟合问题，我们提出了一种自然变形数据增强方法，并根据数据复杂性修补了火车数据动态更新方法，以有效利用有限的数据。最后，我们使用转移学习来协助训练过程并提高模型的鲁棒性。反应：在inbreast数据集上，每个图像平均为0.495误报，而召回率为0.930；在DDSM数据集上，当每个图像具有0.599的误报时，召回率达到0.943。CONCONCLUSIONS：INBREAST和DDSM的实验结果表明，所提出的BMASSDNET可以在当前最高排名的方法上获得竞争性检测性能。
Background and Objective: Accurate detection of breast masses in mammography images is critical to diagnose early breast cancer, which can greatly improve the patients survival rate. However, it is still a big challenge due to the heterogeneity of breast masses and the complexity of their surrounding environment.Methods: To address these problems, we propose a one-stage object detection architecture, called Breast Mass Detection Network (BMassDNet), based on anchor-free and feature pyramid which makes the detection of breast masses of different sizes well adapted. We introduce a truncation normalization method and combine it with adaptive histogram equalization to enhance the contrast between the breast mass and the surrounding environment. Meanwhile, to solve the overfitting problem caused by small data size, we propose a natural deformation data augmentation method and mend the train data dynamic updating method based on the data complexity to effectively utilize the limited data. Finally, we use transfer learning to assist the training process and to improve the robustness of the model ulteriorly.Results: On the INbreast dataset, each image has an average of 0.495 false positives whilst the recall rate is 0.930; On the DDSM dataset, when each image has 0.599 false positives, the recall rate reaches 0.943.Conclusions: The experimental results on datasets INbreast and DDSM show that the proposed BMassDNet can obtain competitive detection performance over the current top ranked methods.
