Alzheimer's disease (AD) is a neurodegenerative disorder that affects the life quality of millions of people worldwide. To diagnose new cases in a timely manner, we propose a new novelty detection technique that combines Autoencoder and Minimum Covariance Determinant (MCD). The technique consists of two steps: first, we use an Autoencoder to extract low-dimensional and discriminative features from the publicly available ADNI dataset, where we only train the Autoencoder with normal data, making the abnormal data more distinguishable in the feature space; second, based on the features of normal data, we use MCD to construct a decision boundary, and judge the degree of abnormality by the distance of the test point to the boundary. Compared with traditional methods without using Autoencoder, our technique has significant advantages in terms of accuracy and sensitivity, and can effectively deal with data imbalance problem. Experimental results show that our method can efficiently detect novel AD cases, and has a wide range of application prospects.
Alzheimer's disease (AD) is a common neurodegenerative disease, whose early diagnosis is crucial for disease control and treatment. This study aims to explore the use of ensemble learning to analyze data from AD patients using multimodal inputs, including MRI image features extracted by convolutional neural networks (CNN), age, gender, APOE status and clinical functional scales. Firstly, we preprocess and extract the key image information features related to AD from MRI images. We then used multiple machine learning (ML) methods to build different classifiers, and combined these different classifiers by voting to obtain more accurate prediction results. Our method has been validated on a large AD patient database.The results demonstrated that the analysis of multimodal data can significantly improve the diagnostic accuracy of AD compared to single-mode data, while ensemble learning further improves the stability of the model.
The development of tumor is closely related to extracellular matrix, which changes the biomechanical behavior of cells.Research have prepared polyacrylamide hydrogel substrates of differing stiffness according to the hardness values of breast tissue under normal and tumor physiological conditions. Then AFM was used to measure the mechanical properties of breast cells with different degrees of malignancy grown on different stiffness substrates. To explore the reasons for the changes in the young’s modulus of three breast cells, the distribution of cellular actin filaments were observed with a confocal microscope. These results showed that when the substrate hardened, the viscoelasticity of benign breast cells increased significantly, and the other two cancer cells also changed to some extent. We also found that the harder the substrate, the more conducive to the spreading behavior of cells, and the weaker response of malignant cells to substrates.
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