Lightweight neural network for Alzheimer's disease classification using multi-slice sMRI.

Alzheimer's disease (AD) is a progressive neurodegenerative disease. Early detection and intervention are crucial in preventing the progression of AD. To achieve efficient and scalable AD auto-detection based on structural Magnetic Resonance Imaging (sMRI), a lightweight neural network using multi-slice sMRI is proposed in this paper.

Prototype-guided multi-scale domain adaptation for Alzheimer's disease detection.

Alzheimer's disease (AD) is the most common form of dementia and there is no effective treatment currently. Using artificial intelligence technology to assist the diagnosis and intervention as early as possible is of great significance to delay the development of AD. Structural Magnetic Resonance Imaging (sMRI) has shown great practical values on computer-aided AD diagnosis.

Discriminative Analysis of Migraine without Aura: Using Functional and Structural MRI with a Multi-Feature Classification Approach.

Magnetic resonance imaging (MRI) is by nature a multi-modality technique that provides complementary information about different aspects of diseases. So far no attempts have been reported to assess the potential of multi-modal MRI in discriminating individuals with and without migraine, so in this study, we proposed a classification approach to examine whether or not the integration of multiple MRI features could improve the classification performance between migraine patients without aura (MWoA) and healthy controls. Twenty-one MWoA patients and 28 healthy controls participated in this study.

Multimodal MRI-Based Classification of Trauma Survivors with and without Post-Traumatic Stress Disorder.

Post-traumatic stress disorder (PTSD) is a debilitating psychiatric disorder. It can be difficult to discern the symptoms of PTSD and obtain an accurate diagnosis. Different magnetic resonance imaging (MRI) modalities focus on different aspects, which may provide complementary information for PTSD discrimination.

[Head and Neck Tumor Segmentation Based on Augmented Gradient Level Set Method].

To realize the accurate positioning and quantitative volume measurement of tumor in head and neck tumor CT images, we proposed a level set method based on augmented gradient. With the introduction of gradient information in the edge indicator function, our proposed level set model is adaptive to different intensity variation, and achieves accurate tumor segmentation. The segmentation result has been used to calculate tumor volume.

[Mitochondrial 12S rRNA variants studies in 456 subjects with hearing loss in seven schools for deaf and mutes in Zhejiang province].

Objective: To investigate mutational spectrum and frequency of the mitochondrial 12S rRNA gene in Chinese subjects with aminoglycoside-induced and non-syndromic hearing loss.

Methods: Total of 456 subjects with non-syndromic hearing loss were recruited from seven schools for deaf-mutes in Zhejiang province. Genomic DNA was extracted from the whole blood, and then the DNA fragment was amplified spanning the 12S rRNA gene, followed by sequencing and analyzed.