Feature fusion Siamese network for breast cancer detection comparing current and prior mammograms
Feature fusion Siamese network for breast cancer detection comparing current and prior mammograms
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DOI：
10.1002/mp.15598
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发表时间：
2022-04-22
期刊：
影响因子：
3.8
通讯作者：
Nabavi, Sheida
中科院分区：
文献类型：
--
作者：
Bai, Jun;Jin, Annie;Nabavi, Sheida
目的从乳腺X线摄影图像中自动检测非常小和非肿块的异常仍然具有挑战性。在针对每个患者的临床实践中，放射科医师通常不仅筛选在检查期间获得的乳房X线照片图像，而且还将它们与先前的乳房X线照片图像进行比较以做出临床决策。为了设计一个人工智能（AI）系统来模仿放射科医生以更好地检测癌症，在这项工作中，我们提出了一个端到端的增强型暹罗卷积神经网络，用于使用前一年和当年的乳房X光照片图像检测乳腺癌。方法提出的基于Siamese的网络使用高分辨率的乳腺X线照片图像，并融合前一年和今年的乳腺X线照片图像对的特征来预测癌症概率。所提出的方法是基于一次性学习的概念开发的，一次性学习学习当前图像和先前图像之间的异常差异，而不是异常对象，因此可以在小样本数据集上表现得更好。我们开发了拟议网络的两个变体。在第一个模型中，为了融合当前和以前图像的特征，我们设计了一个增强的远程学习网络，它不仅考虑了整体距离，还考虑了特征之间的像素距离。在另一个模型中，我们将当前图像和先前图像的特征连接起来以融合它们。结果我们将所提出的模型的性能与仅使用当前图像（ResNet和VGG）以及使用当前和先前图像（长短期记忆[LSTM]和香草暹罗）的一些基线模型的性能进行了比较，包括准确性，灵敏度，精度，F1评分和曲线下面积（AUC）。结果表明，所提出的模型优于基线模型，所提出的模型与远程学习网络的性能最好（准确度：0.92，灵敏度：0.93，精确度：0.91，特异性：0.91，F1：0.92和AUC：0.95）。结论：整合之前的乳腺X线摄影图像可以提高癌症的自动分类，特别是对于非常小和非肿块的异常。对于集成当前和先前乳房X线照片图像的分类模型，使用增强且有效的远程学习网络可以提高模型的性能。
Purpose Automatic detection of very small and nonmass abnormalities from mammogram images has remained challenging. In clinical practice for each patient, radiologists commonly not only screen the mammogram images obtained during the examination, but also compare them with previous mammogram images to make a clinical decision. To design an artificial intelligence (AI) system to mimic radiologists for better cancer detection, in this work we proposed an end-to-end enhanced Siamese convolutional neural network to detect breast cancer using previous year and current year mammogram images. Methods The proposed Siamese-based network uses high-resolution mammogram images and fuses features of pairs of previous year and current year mammogram images to predict cancer probabilities. The proposed approach is developed based on the concept of one-shot learning that learns the abnormal differences between current and prior images instead of abnormal objects, and as a result can perform better with small sample size data sets. We developed two variants of the proposed network. In the first model, to fuse the features of current and previous images, we designed an enhanced distance learning network that considers not only the overall distance, but also the pixel-wise distances between the features. In the other model, we concatenated the features of current and previous images to fuse them. Results We compared the performance of the proposed models with those of some baseline models that use current images only (ResNet and VGG) and also use current and prior images (long short-term memory [LSTM] and vanilla Siamese) in terms of accuracy, sensitivity, precision, F1 score, and area under the curve (AUC). Results show that the proposed models outperform the baseline models and the proposed model with the distance learning network performs the best (accuracy: 0.92, sensitivity: 0.93, precision: 0.91, specificity: 0.91, F1: 0.92 and AUC: 0.95). Conclusions Integrating prior mammogram images improves automatic cancer classification, specially for very small and nonmass abnormalities. For classification models that integrate current and prior mammogram images, using an enhanced and effective distance learning network can advance the performance of the models.