Background and ObjectivesSegmentation of mammographic lesions has been proven to be a valuable source of information, as it can assist in both extracting shape-related features and providing accurate localization of the lesion. In this work, a methodology is proposed for integrating mammographic mass segmentation information into a convolutional neural network (CNN), aiming to improve the diagnosis of breast cancer in mammograms. MethodsThe proposed methodology involves modification of each convolutional layer of a CNN, so that information of not only the input image but also the corresponding segmentation map is considered. Furthermore, a new loss function is introduced, which adds an extra term to the standard cross-entropy, aiming to steer the attention of the network to the mass region, penalizing strong feature activations based on their location. The segmentation maps are acquired either from the provided ground-truth or from an automatic segmentation stage. ResultsPerformance evaluation in diagnosis is conducted on two mammographic mass datasets, namely DDSM-400 and CBIS-DDSM, with differences in quality of the corresponding ground-truth segmentation maps. The proposed method achieves diagnosis performance of 0.898 and 0.862 in terms AUC when using ground-truth segmentation maps and a maximum of 0.880 and 0.860 when a U-Net-based automatic segmentation stage is employed, for DDSM-400 and CBIS-DDSM, respectively. ConclusionsThe experimental results demonstrate that integrating segmentation information into a CNN leads to improved performance in breast cancer diagnosis of mammographic masses.

背景与目的乳腺钼靶病变的分割已被证明是有价值的信息来源，因为它可以帮助提取与形状相关的特征并提供病变的准确定位。在这项工作中，提出了一种将乳腺X线摄影质量分割信息集成到卷积神经网络（CNN）中的方法，旨在改善乳腺X线照片中对乳腺癌的诊断。方法所提出的方法涉及对CNN的每个卷积层的修改，因此不仅要考虑输入图像的信息，还要考虑相应的分割图。此外，引入了新的损失函数，该函数为标准交叉熵增加了一个附加项，目的是将网络的注意力引向质量区域，从而根据其位置对强大的特征激活进行惩罚。从提供的地面真相或从自动分段阶段获取分段图。结果诊断的性能评估是在两个乳房X线摄影质量数据集DDSM-400和CBIS-DDSM上进行的，相应的地面真相分割图的质量有所不同。对于DDSM-400和CBIS-DDSM，所提出的方法在使用地面真实分割图时的诊断性能分别为AUC的0.898和0.862，在使用基于U-Net的自动分割阶段时的诊断性能分别为0.880和0.860。 。结论实验结果表明，将分割信息整合到CNN中可以提高乳腺X线摄影肿块的乳腺癌诊断性能。