AIMC Topic: Multimodal Imaging

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Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer's Disease Diagnosis.

Neuroinformatics
Accurate and early diagnosis of Alzheimer's disease (AD) plays important role for patient care and development of future treatment. Structural and functional neuroimages, such as magnetic resonance images (MRI) and positron emission tomography (PET),...

Cross-Modality Image Synthesis via Weakly Coupled and Geometry Co-Regularized Joint Dictionary Learning.

IEEE transactions on medical imaging
Multi-modality medical imaging is increasingly used for comprehensive assessment of complex diseases in either diagnostic examinations or as part of medical research trials. Different imaging modalities provide complementary information about living ...

Convolutional Invasion and Expansion Networks for Tumor Growth Prediction.

IEEE transactions on medical imaging
Tumor growth is associated with cell invasion and mass-effect, which are traditionally formulated by mathematical models, namely reaction-diffusion equations and biomechanics. Such models can be personalized based on clinical measurements to build th...

Label-aligned multi-task feature learning for multimodal classification of Alzheimer's disease and mild cognitive impairment.

Brain imaging and behavior
Multimodal classification methods using different modalities of imaging and non-imaging data have recently shown great advantages over traditional single-modality-based ones for diagnosis and prognosis of Alzheimer's disease (AD), as well as its prod...

A comparison of multimodal biomarkers for chronic hepatitis B assessment using recursive feature elimination.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
An effective assessment of liver fibrosis in patients with chronic hepatitis B (CHB) is highly desired because it is important not only for clinical courses prediction, but also for the determination of antiviral therapy schemes. In recent years, var...

Feature-Motivated Simplified Adaptive PCNN-Based Medical Image Fusion Algorithm in NSST Domain.

Journal of digital imaging
Multimodality medical image fusion plays a vital role in diagnosis, treatment planning, and follow-up studies of various diseases. It provides a composite image containing critical information of source images required for better localization and def...

Multimodal manifold-regularized transfer learning for MCI conversion prediction.

Brain imaging and behavior
As the early stage of Alzheimer's disease (AD), mild cognitive impairment (MCI) has high chance to convert to AD. Effective prediction of such conversion from MCI to AD is of great importance for early diagnosis of AD and also for evaluating AD risk ...

[A Case of Granulocyte-Colony Stimulating Factor-Producing Gastric Cancer Successfully Treated with Trastuzumab].

Gan to kagaku ryoho. Cancer & chemotherapy
A 68-year-old man diagnosed with type 0-Ⅰgastric cancer by gastrointestinal endoscopy underwent urgent distal gastrectomy due to a perforation during endoscopic submucosal resection. Pathological examination revealed pT3N2M0, pStage ⅢA. TS-1 was admi...

Identification of Conversion from Normal Elderly Cognition to Alzheimer's Disease using Multimodal Support Vector Machine.

Journal of Alzheimer's disease : JAD
Alzheimer's disease (AD) is one of the most serious progressive neurodegenerative diseases among the elderly, therefore the identification of conversion to AD at the earlier stage has become a crucial issue. In this study, we applied multimodal suppo...