State-of-the-art deep learning methods for image processing are evolving into increasingly complex meta-architectures with a growing number of modules. Among them, region-based fully convolutional networks (R-FCN) and deformable convolutional nets (DCN) can improve CAD for mammography: R-FCN optimizes for speed and low consumption of memory, which is crucial for processing the high resolutions of to 50 micrometers used by radiologists. Deformable convolution and pooling can model a wide range of mammographic findings of different morphology and scales, thanks to their versatility. In this study, we present a neural net architecture based on R-FCN / DCN, that we have adapted from the natural image domain to suit mammograms -- particularly their larger image size -- without compromising resolution. We trained the network on a large, recently released dataset (Optimam) including 6,500 cancerous mammograms. By combining our modern architecture with such a rich dataset, we achieved an area under the ROC curve of 0.879 for breast-wise detection in the DREAMS challenge (130,000 withheld images), which surpassed all other submissions in the competitive phase.
翻译:用于图像处理的最先进的深层学习方法正在演变成日益复杂的元结构,其模块数量越来越多,其中,基于区域的全面革命网络(R-FCN)和变形革命网(DCN)可以改进乳房XMXXX:R-FCN优化速度和低记忆消耗率,这对于处理放射学家使用的高达50微米的高分辨率至关重要。变形混成和汇集可以建模不同形态和规模的广泛的乳房摄影结果,这要归功于它们的多功能性。在这项研究中,我们展示了以R-FCN/DCN为基础的神经网结构,我们从自然图像领域调整了神经网结构,以适应乳房X光图 -- -- 尤其是其更大的图像大小 -- -- 而不影响分辨率。我们就最近发布的大型数据集(Opimimimimim)对网络进行了培训,其中包括6 500张癌症乳房图。通过将我们的现代结构与如此丰富的数据集相结合,我们在ROC的0.879曲线下,在DREMS提交的所有竞争性图像保存阶段(130张)中,我们取得了超越了其他阶段。