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Medical content-based retrieval (MCBR) plays an important role in computer aided diagnosis and clinical decision support. Multi-modal imaging data have been increasingly used in MCBR, as they could provide more insights of the diseases and complement the deficiencies of single-modal data. However, it is very challenging to fuse data in different modalities since they have different physical fundamentals...
The accurate diagnosis of Alzheimer's disease (AD) plays a significant role in patient care, especially at the early stage, because the consciousness of the severity and the progression risks allows the patients to take prevention measures before irreversible brain damages are shaped. Although many studies have applied machine learning methods for computer-aided-diagnosis (CAD) of AD recently, a bottleneck...
Multimodal medical data from various information sources are often used to depict patients. We refer to each source as a ‘view’. Multi-view features could provide complementary information to each other; thus by fusing the multi-view features, we could greatly enhance the current medical content-based retrieval framework. In this paper, we propose a Co-neighbor Multi-view Spectral Embedding (CMSE)...
The multi-view/multi-modal features are commonly used in neuroimaging classification because they could provide complementary information to each other and thus result in better classification performance than single-view features. However, it is very challenging to effectively integrate such rich features, since straightforward concatenation or singleview spectral embedding methods rarely leads to...
The high throughput 3D neuroimaging datasets have posed great challenges for neuroimaging data retrieval. To achieve more accurate neuroimaging retrieval, various content-based retrieval approaches have been proposed. Recent studies showed a tendency of using the localized features extracted from a subset of the brain structures, instead of the global features extracted from whole brain. However,...
The prediction of Alzheimer's disease (AD) severity is very important in AD diagnosis and patient care, especially for patients at early stage when clinical intervention is most effective and no irreversible damages have been formed to brains. To achieve accurate diagnosis of AD and identify the subjects who have higher risk to convert to AD, we proposed an AD severity prediction method based on the...
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