A Transfer Learning Approach for Early Diagnosis of Alzheimer's Disease on MRI Images.

BACKGROUND An increase in lifespan in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer's disease being the most prevalent. Advances in medical imaging and computational power enable new methods for the early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer's disease dementia. METHODS We conducted a systematic review following PRISMA guidelines of studies where machine learning was applied to neuroimaging data in order to predict whether patients with mild cognitive impairment might develop Alzheimer's disease dementia or remain stable. After removing duplicates, we screened 452 studies and selected 116 for qualitative analysis. RESULTS Most studies used magnetic resonance image (MRI) and positron emission tomography (PET) data but also magnetoencephalography. The datasets were mainly extracted from the Alzheimer's disease neuroimaging initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common was support vector machine with a mean accuracy of 75.4%, but convolutional neural networks achieved a higher mean accuracy of 78.5%. Studies combining MRI and PET achieved overall better classification accuracy than studies that only used one neuroimaging technique. In general, the more complex models such as those based on deep learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, genetic, and behavioral) achieved the best performance. CONCLUSIONS Although the performance of the different methods still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.

/pdf/machine-learning-methods-for-predicting-progression-from-4ouw9072j3.pdf

Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer’s disease dementia: a systematic review

It has been more than 30 years since the French pioneered the research of the apple harvesting robot, and with the joint efforts of scholars all around the world, a variety of prototypes have been ...

https://journals.sagepub.com/doi/pdf/10.1177/1729881420925310

Apple harvesting robot under information technology: A review:

If an organization depends on the service quality provided by another organization it often enters into a bilateral service level agreement (SLA), which mitigates outsourcing risks by associating penalty payments with poor service quality. Once these agreements are entered into, it becomes necessary to monitor their conditions, which will commonly relate to timeliness, reliability and request throughput, at run-time. We show how these conditions can be translated into timed automata. Acceptance of a timed word by a timed automaton can be decided in quadratic time and because the timed automata can operate while messages are exchanged at run-time there is effectively only a linear run-time overhead. We present an implementation to derive on-line monitors for web services automatically from SLAs using an Eclipse plugin. We evaluate the efficiency and scalability of this approach using a large-scale case study in a service-oriented computational grid.

Efficient Online Monitoring of Web-Service SLAs

It is of great significance to apply deep learning for the early diagnosis of Alzheimer's Disease (AD). In this work, a novel tensorizing GAN with high-order pooling is proposed to assess Mild Cognitive Impairment (MCI) and AD. By tensorizing a three-player cooperative game based framework, the proposed model can benefit from the structural information of the brain. By incorporating the high-order pooling scheme into the classifier, the proposed model can make full use of the second-order statistics of the holistic Magnetic Resonance Imaging (MRI) images. To the best of our knowledge, the proposed Tensor-train, High-pooling and Semi-supervised learning based GAN (THS-GAN) is the first work to deal with classification on MRI images for AD diagnosis. Extensive experimental results on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset are reported to demonstrate that the proposed THS-GAN achieves superior performance compared with existing methods, and to show that both tensor-train and high-order pooling can enhance classification performance. The visualization of generated samples also shows that the proposed model can generate plausible samples for semi-supervised learning purpose.

Tensorizing GAN with High-Order Pooling for Alzheimer's Disease Assessment

Neuroimaging techniques have been used for automatic diagnosis and classification of Alzheimer's disease and mild cognitive impairment. How to select discriminant features from these data is the key that will affect the subsequent automatic diagnosis and classification performance. However, in the previous manifold regularized sparse regression models, the local neighborhood structure was constructed directly in the traditional Euclidean distance without fully utilizing the label information of the subjects, which leads to the selection of less discriminative features. In this paper, we propose a novel manifold regularized sparse regression model for learning discriminative features. Specifically, we first adopt l
 2,1
-norm regularization to jointly select a relevant feature subset among the samples. Then, to select more discriminative features, a novel manifold regularization term is constructed via the relative distance adjusted by the label information, which can simultaneously maintain the compactness of the intra-class samples and the separability of inter-class samples. The proposed feature learning method is further carried out for both the binary classification and the multi-class classification. The experimental results on Alzheimer's Disease Neuroimaging Initiative database demonstrate the effectiveness of the proposed method, which can be utilized for the diagnosis of Alzheimer's disease and mild cognitive impairment.

/pdf/sparse-feature-learning-with-label-information-for-alzheimer-m1ftcto2fn.pdf

Sparse Feature Learning With Label Information for Alzheimer’s Disease Classification Based on Magnetic Resonance Imaging

Current QoS-aware automatic service composition queries over a network of web services are often one-time in nature. After a network of web services is built, such queries are issued once, and answers are found from the scratch. The underlying assumption is that the participating web services are rather static , which means that their functional and non-functional parameters seldom change. However, such an assumption is often baseless. New services come and go, service APIs change gradually, and QoS values fluctuate in practice. Therefore, a support for efficiently adapting service composition is desired. In this paper, we propose an event driven continuous query algorithm to intelligently cope with different types of dynamic services, and thus enable evolution of service composition. Evaluation using both real QoS data and synthetic web service data shows its superior performance, compared with the state-of-the-art solution which won the performance championship of Web Service Challenge in 2009 and 2010. Moreover, we generalize a new graph problem: dynamic single-source optimal-Directed Acyclic Graphs (DAG) problem based on the above work. It is argued that API recommendation for framework evolution, as another typical application, could also be modeled and addressed efficiently using the proposed new graph approach.

Efficient Dynamic Evolution of Service Composition

The dynamic interventions among sensor streams bring new challenges for the Internet of Things applications to derive meaningful information from a large amount of sensor streams. This paper proposes a declarative service-based method for capturing meaningful events from dynamic sensor streams based on our previous service abstraction, which can increase the value density of primitive sensor streams and raise the accessibility for higher level business applications. For aggregating related events from different sources, we set up declarative rules and compose cascaded events through service collaboration. In addition, for dealing with dynamic interventions among sensor streams, we propose a declarative rule evolution method to realize adaptive service collaboration at runtime. This paper also reports the tryout use of our method in the China Power Grid for detecting abnormal situations of power quality. Through a series of experiments based on real sensor data in the Power Grid, we verified that our method can capture events with high and steady precision.

/pdf/a-declarative-service-based-method-for-adaptive-aggregation-1x7g9gdhq3.pdf

A Declarative Service-Based Method for Adaptive Aggregation of Sensor Streams

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Sparse Feature Learning With Label Information for Alzheimer’s Disease Classification Based on Magnetic Resonance Imaging

Efficient Dynamic Evolution of Service Composition

A Declarative Service-Based Method for Adaptive Aggregation of Sensor Streams