Automatic Scoring of Multiple Semantic Attributes With Multi-Task Feature Leverage: A Study on Pulmonary Nodules in CT Images
Automatic Scoring of Multiple Semantic Attributes With Multi-Task Feature Leverage: A Study on Pulmonary Nodules in CT Images
复制标题
利用多任务特征杠杆对多个语义属性进行自动评分：CT 图像中肺部结节的研究
DOI：
10.1109/tmi.2016.2629462
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发表时间：
2017-03-01
影响因子：
10.6
通讯作者：
Cheng, Jie-Zhi
中科院分区：
文献类型：
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作者：
Chen, Sihong;Qin, Jing;Cheng, Jie-Zhi
计算特征和语义特征之间的差距是制约计算机辅助诊断（CAD）性能的主要因素之一。为了弥补这一差距，我们利用三种多任务学习（MTL）方案来利用来自堆叠去噪自动编码器（SDAE）和卷积神经网络（CNN）深度学习模型的异构计算特征，以及手工制作的haar样特征和HoG特征，来描述CT图像中肺结节的9个语义特征。我们认为“spiculation”、“texture”、“margin”等语义特征之间可能存在关系，可以通过MTL来探索。本研究采用了肺图像数据库联盟（LIDC）的数据，其注释资源丰富。通过将每个语义特征视为单独的任务，MTL方案选择异构计算特征并将其映射到放射科医生的评分，并对从LIDC数据集中随机选择的2400个结节进行交叉验证评估方案。实验结果表明，与单任务LASSO和弹性网络回归方法相比，三种MTL方案预测的语义分数更接近放射科医生的评分。提出的语义属性评分方案可为结节提供更丰富的定量评价，为诊断决策和管理提供更好的支持。同时，该方法对医学图像内容与临床语义术语的自动关联能力也有助于医学搜索引擎的开发。
The gap between the computational and semantic features is the one of major factors that bottlenecks the computer-aided diagnosis (CAD) performance from clinical usage. To bridge this gap, we exploit three multi-task learning (MTL) schemes to leverage heterogeneous computational features derived from deep learning models of stacked denoising autoencoder (SDAE) and convolutional neural network (CNN), as well as hand-crafted Haar-like and HoG features, for the description of 9 semantic features for lung nodules in CT images. We regard that there may exist relations among the semantic features of “spiculation”, “texture”, “margin”, etc., that can be explored with the MTL. The Lung Image Database Consortium (LIDC) data is adopted in this study for the rich annotation resources. The LIDC nodules were quantitatively scored w.r.t. 9 semantic features from 12 radiologists of several institutes in U.S.A. By treating each semantic feature as an individual task, the MTL schemes select and map the heterogeneous computational features toward the radiologists’ ratings with cross validation evaluation schemes on the randomly selected 2400 nodules from the LIDC dataset. The experimental results suggest that the predicted semantic scores from the three MTL schemes are closer to the radiologists’ ratings than the scores from single-task LASSO and elastic net regression methods. The proposed semantic attribute scoring scheme may provide richer quantitative assessments of nodules for better support of diagnostic decision and management. Meanwhile, the capability of the automatic association of medical image contents with the clinical semantic terms by our method may also assist the development of medical search engine.