论文标题
使用公正的浅关键点和深CNN功能对通用医学图像数据进行大规模索引
Large Scale Indexing of Generic Medical Image Data using Unbiased Shallow Keypoints and Deep CNN Features
论文作者
论文摘要
我们提出了一个统一的外观模型，该模型对传统的浅层（即3D筛分关键点）和深（即CNN输出层）图像特征表示，分别编码特定的特定，局部的神经解剖模式，并将丰富的全局信息编码为单个索引和分类框架。一种新颖的贝叶斯模型根据有条件独立性的假设结合了浅和深度特征，并通过对特定家庭成员的特定家庭成员和一般组类别进行了验证，其中包括人类连接群岛项目的1010名受试者的3D MRI神经图像数据，包括双胞胎和非双胞胎兄弟姐妹。提出了一种新型的域适应策略，将深层CNN矢量元素转化为二进制类信息描述符。提供了基于GPU的所有处理实施。在计算复杂性，识别家庭成员和性别分类方面的准确性方面，在大规模神经图像索引中都实现了最先进的表现。
We propose a unified appearance model accounting for traditional shallow (i.e. 3D SIFT keypoints) and deep (i.e. CNN output layers) image feature representations, encoding respectively specific, localized neuroanatomical patterns and rich global information into a single indexing and classification framework. A novel Bayesian model combines shallow and deep features based on an assumption of conditional independence and validated by experiments indexing specific family members and general group categories in 3D MRI neuroimage data of 1010 subjects from the Human Connectome Project, including twins and non-twin siblings. A novel domain adaptation strategy is presented, transforming deep CNN vectors elements into binary class-informative descriptors. A GPU-based implementation of all processing is provided. State-of-the-art performance is achieved in large-scale neuroimage indexing, both in terms of computational complexity, accuracy in identifying family members and sex classification.
