论文标题
Nodule2Vec：使用语义表示的3D深度学习系统用于肺结结节检索
Nodule2vec: a 3D Deep Learning System for Pulmonary Nodule Retrieval Using Semantic Representation
论文作者
论文摘要
基于内容的检索通过向医生展示包含历史诊断和进一步疾病发育史的数据库中最相似的病例来支持放射科医生的决策过程。我们提出了一个深度学习系统，该系统将肺结核的3D图像从CT扫描转换为低维嵌入载体。我们证明，这样的矢量表示保留了有关结节的语义信息，并为基于内容的图像检索提供了可行的方法（CBIR）。我们讨论了可用数据集的理论局限性，并通过应用最先进的肺结节检测模型的转移学习来克服它们。我们使用胸CT扫描的LIDC-IDRI数据集评估了系统。我们设计了一个相似性评分，并表明它可以用来测量相似性1）不同放射科医生在同一结节的注释之间和2）查询结节和前四个CBIR结果之间的相似性。医生和算法分数之间的比较表明，该系统提供给放射科医生最终用户的收益与获得第二射线科医生的意见相当。
Content-based retrieval supports a radiologist decision making process by presenting the doctor the most similar cases from the database containing both historical diagnosis and further disease development history. We present a deep learning system that transforms a 3D image of a pulmonary nodule from a CT scan into a low-dimensional embedding vector. We demonstrate that such a vector representation preserves semantic information about the nodule and offers a viable approach for content-based image retrieval (CBIR). We discuss the theoretical limitations of the available datasets and overcome them by applying transfer learning of the state-of-the-art lung nodule detection model. We evaluate the system using the LIDC-IDRI dataset of thoracic CT scans. We devise a similarity score and show that it can be utilized to measure similarity 1) between annotations of the same nodule by different radiologists and 2) between the query nodule and the top four CBIR results. A comparison between doctors and algorithm scores suggests that the benefit provided by the system to the radiologist end-user is comparable to obtaining a second radiologist's opinion.
