论文标题
哺乳动物：放射学教育的高分辨率乳房X线照片受控生成
MammoGANesis: Controlled Generation of High-Resolution Mammograms for Radiology Education
论文作者
论文摘要
在成长的几年中，放射学学员每月必须解释数百次乳房X线照片，目的是恰当地辨别出与恶性病变不同的微妙模式。不幸的是，医学法律和技术障碍使得难以访问和查询医疗图像进行培训。 在本文中，我们训练生成对抗网络（GAN）合成512 x 512高分辨率乳房X线照片。最终的模型导致了高级特征的无监督分离（例如，标准的乳房摄影视图和乳房病变的性质），生成的图像（例如乳腺脂肪组织，钙化）的随机变化，使用户控制的全球和本地属性构成合成图像的图像。 我们通过对四位专家乳房X线X线摄影放射学家的双盲研究实现了平均AUC，以分辨出生成的图像和真实图像，从而归因于合成和编辑的医学教育的潜在使用，从而区分了生成和真实的图像，从而证明了该模型的能力，可以通过对四位专家乳房X线摄影放射学家的平均AUC进行0.54的平均AUC进行，以及归因于高视觉质量。
During their formative years, radiology trainees are required to interpret hundreds of mammograms per month, with the objective of becoming apt at discerning the subtle patterns differentiating benign from malignant lesions. Unfortunately, medico-legal and technical hurdles make it difficult to access and query medical images for training. In this paper we train a generative adversarial network (GAN) to synthesize 512 x 512 high-resolution mammograms. The resulting model leads to the unsupervised separation of high-level features (e.g. the standard mammography views and the nature of the breast lesions), with stochastic variation in the generated images (e.g. breast adipose tissue, calcification), enabling user-controlled global and local attribute-editing of the synthesized images. We demonstrate the model's ability to generate anatomically and medically relevant mammograms by achieving an average AUC of 0.54 in a double-blind study on four expert mammography radiologists to distinguish between generated and real images, ascribing to the high visual quality of the synthesized and edited mammograms, and to their potential use in advancing and facilitating medical education.
