论文标题
学习具有病理学的真实癌组织的低维流形
Learning a low dimensional manifold of real cancer tissue with PathologyGAN
论文作者
论文摘要
深度学习在数字病理学中的应用显示出有望改善疾病诊断和理解。我们提出了一个深层生成模型，该模型学会了模拟高保真癌组织图像，同时将真实图像映射到可解释的低维潜在空间上。该模型的关键是由先前开发的生成对抗网络Pathologygan训练的编码器。我们使用来自两个乳腺癌队列的249K图像研究潜在空间。我们发现潜在空间编码组织的形态特征（例如癌症，淋巴细胞和基质细胞的模式）。此外，潜在空间在高危患者组中揭示了明显丰富的组织结构簇。
Application of deep learning in digital pathology shows promise on improving disease diagnosis and understanding. We present a deep generative model that learns to simulate high-fidelity cancer tissue images while mapping the real images onto an interpretable low dimensional latent space. The key to the model is an encoder trained by a previously developed generative adversarial network, PathologyGAN. We study the latent space using 249K images from two breast cancer cohorts. We find that the latent space encodes morphological characteristics of tissues (e.g. patterns of cancer, lymphocytes, and stromal cells). In addition, the latent space reveals distinctly enriched clusters of tissue architectures in the high-risk patient group.
