论文标题
无监督的深度聚类，用于医学图像中预测性纹理模式发现
Unsupervised deep clustering for predictive texture pattern discovery in medical images
论文作者
论文摘要
成像数据中的预测标记模式是量化疾病和进展的一种手段，但是如果了解潜在的生物学知识不足，它们的鉴定是具有挑战性的。在这里，我们提出了一种以无监督的方式识别医学图像中预测性纹理模式的方法。基于深层聚类网络，我们同时在低维的潜在空间中编码和聚类医学图像补丁。由此产生的簇是疾病分期的特征，将其与潜在疾病联系起来。我们评估了具有不同肝脏脂肪变性阶段的患者的70个加权磁共振图像的方法。深度聚类方法能够找到具有稳定排名的预测簇，在低脂肪变性和高脂肪变性的情况下以0.78的F1分数区分。
Predictive marker patterns in imaging data are a means to quantify disease and progression, but their identification is challenging, if the underlying biology is poorly understood. Here, we present a method to identify predictive texture patterns in medical images in an unsupervised way. Based on deep clustering networks, we simultaneously encode and cluster medical image patches in a low-dimensional latent space. The resulting clusters serve as features for disease staging, linking them to the underlying disease. We evaluate the method on 70 T1-weighted magnetic resonance images of patients with different stages of liver steatosis. The deep clustering approach is able to find predictive clusters with a stable ranking, differentiating between low and high steatosis with an F1-Score of 0.78.
