Designing computer-assisted diagnosis (CAD) systems that can precisely identify lesions from mammography images would be useful for clinicians. Considering the morphological variation in breast cancer, it is necessary to extract robust features from the mammogram. Here, we propose a mass detection CAD system that is based on Faster R-CNN. First, we applied a novel convolution network in the backbone of Faster R-CNN, namely deformable convolution network (DCN), which improves the detection of lesions with varying shapes and sizes. Second, the original Faster R-CNN uses the output of the last layer of the backbone as a single-scale feature map. To facilitate the detection of small lesions, we used a multiscale feature pyramid network of multiple cross-scale connections between the different output layers of the backbone, called the neural architecture search-feature pyramid network (NAS-FPN). Thus, we were able to integrate the best features into the model. We then evaluated our method by using the datasets the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast, respectively. Our method yielded a true positive rate of 0.9345 at 2.2805 false positive per image on CBIS-DDSM and a true positive rate of 0.9554 at 0.3829 false positive per image on INbreast. Graphical abstract.

中文翻译：

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使用可变形卷积和多尺度功能自动进行乳房X线摄影质量检测。

设计能够从乳房X线照相图像中准确识别病变的计算机辅助诊断（CAD）系统对临床医生将很有用。考虑到乳腺癌的形态变化，有必要从乳房X线照片中提取可靠的特征。在这里，我们提出了一种基于Faster R-CNN的质量检测CAD系统。首先，我们在Faster R-CNN的主干中应用了一种新颖的卷积网络，即可变形卷积网络（DCN），它改善了形状和大小各异的病变的检测。其次，原始的Faster R-CNN使用主干最后一层的输出作为单尺度特征图。为了便于发现小病灶，我们在骨干的不同输出层之间使用了多个跨尺度连接的多尺度特征金字塔网络，称为神经结构搜索功能金字塔网络（NAS-FPN）。因此，我们能够将最佳功能集成到模型中。然后，我们分别通过使用乳腺X线摄影筛查数字数据库（CBIS-DDSM）和INbreast的数据集评估了我们的方法。我们的方法在CBIS-DDSM上每个图像产生2.2805假阳性的真实阳性率为0.9345，在INbreast上每个图像产生0.3829假阳性的真实阳性率为0.9554。图形概要。CBIS-DDSM上每个图像2805次假阳性，而INbreast上每个图像0.3829个假阳性的真实阳性率为0.9554。图形概要。CBIS-DDSM上每个图像2805次假阳性，而INbreast上每个图像0.3829个假阳性的真实阳性率为0.9554。图形概要。