In recent years, the use of Convolutional Neural Networks (CNNs) in medical imaging has shown improved performance in terms of mass detection and classification compared to current state-of-the-art methods. This paper proposes a fully automated framework to detect masses in Full-Field Digital Mammograms (FFDM). This is based on the Faster Region-based Convolutional Neural Network (Faster-RCNN) model and is applied for detecting masses in the large-scale OPTIMAM Mammography Image Database (OMI-DB), which consists of ∼80,000 FFDMs mainly from Hologic and General Electric (GE) scanners. This research is the first to benchmark the performance of deep learning on OMI-DB. The proposed framework obtained a True Positive Rate (TPR) of 0.93 at 0.78 False Positive per Image (FPI) on FFDMs from the Hologic scanner. Transfer learning is then used in the Faster R-CNN model trained on Hologic images to detect masses in smaller databases containing FFDMs from the GE scanner and another public dataset INbreast (Siemens scanner). The detection framework obtained a TPR of 0.91±0.06 at 1.69 FPI for images from the GE scanner and also showed higher performance compared to state-of-the-art methods on the INbreast dataset, obtaining a TPR of 0.99±0.03 at 1.17 FPI for malignant and 0.85±0.08 at 1.0 FPI for benign masses, showing the potential to be used as part of an advanced CAD system for breast cancer screening.

中文翻译：

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全场数字乳房 X 光检查中大规模检测的深度学习。


近年来，与当前最先进的方法相比，卷积神经网络（CNN）在医学成像中的使用在大规模检测和分类方面显示出了改进的性能。本文提出了一种全自动框架来检测全场数字乳房 X 光检查 (FFDM) 中的质量。该模型基于更快的基于区域的卷积神经网络 (Faster-RCNN) 模型，用于检测大型 OPTIMAM 乳腺 X 线摄影图像数据库 (OMI-DB) 中的肿块，该数据库由主要来自 Hologic 和 General 的约 80,000 个 FFDM 组成电动 (GE) 扫描仪。这项研究首次在 OMI-DB 上对深度学习的性能进行了基准测试。所提出的框架在来自 Hologic 扫描仪的 FFDM 上获得了 0.93 的真阳性率 (TPR)，每幅图像的假阳性 (FPI) 为 0.78。然后，在基于 Hologic 图像训练的 Faster R-CNN 模型中使用迁移学习，以检测包含来自 GE 扫描仪和另一个公共数据集 INbreast（西门子扫描仪）的 FFDM 的较小数据库中的质量。对于来自 GE 扫描仪的图像，检测框架在 1.69 FPI 下获得了 0.91±0.06 的 TPR，并且与 INbreast 数据集上最先进的方法相比，还表现出更高的性能，在 1.17 FPI 下获得了 0.99±0.03 的 TPR恶性肿块在 1.0 FPI 时为 0.85±0.08，良性肿块为 0.85±0.08，显示出可用作乳腺癌筛查的先进 CAD 系统的一部分。