Computer-aided detection systems based on deep learning have shown good performance in breast cancer detection. However, high-density breasts show poorer detection performance since dense tissues can mask or even simulate masses. Therefore, the sensitivity of mammography for breast cancer detection can be reduced by more than 20% in dense breasts. Additionally, extremely dense cases reported an increased risk of cancer compared to low-density breasts. This study aims to improve the mass detection performance in highdensity breasts using synthetic high-density full-field digital mammograms (FFDM) as data augmentation during breast mass detection model training. To this end, a total of five cycle-consistent GAN (CycleGAN) models using three FFDM datasets were trained for low-to-high-density image translation in highresolution mammograms. The training images were split by breast density BIRADS categories, being BI-RADS A almost entirely fatty and BI-RADS D extremely dense breasts. Our results showed that the proposed data augmentation technique improved the sensitivity and precision of mass detection in models trained with small datasets and improved the domain generalization of the models trained with large databases. In addition, the clinical realism of the synthetic images was evaluated in a reader study involving two expert radiologists and one surgical oncologist.
翻译:以深层学习为基础的计算机辅助检测系统在乳腺癌检测方面表现良好,然而,高密度乳房显示检测性能较差,因为稠密组织可以掩盖甚至模拟质量,因此,在密度大的乳房中,乳癌检测乳房造影的灵敏度可以降低20%以上;此外,极为密集的病例报告,与低密度乳房相比,癌症风险增加;这项研究的目的是提高高密度乳房中使用合成高密度全场数字乳房X光照片(FFDM)进行大规模检测的性能,作为乳房大规模检测模型培训期间的数据增强;为此,使用三套FFDM数据集的五个周期兼容性GAN(CycleGAN)模型共接受了培训,用于高分辨率乳房造影的低至高密度图像翻译;培训图像被乳房密度BIRADS分类(BI-RADS A 几乎完全脂肪型)和BI-RADS D 极稠密的乳房。我们的调查结果显示,拟议的数据增强技术提高了在经过小数据组培训的模型中进行大规模检测的敏感度和精确度。