ATTENTION U-NET BASED MULTI-VIEW CLUSTERING MODEL FOR PREDICTING ALZHEIMER’S DISEASE PROGRESSION

Abstract

The invention describes an Attention U-Net Based Multi-View Clustering Model for Predicting Alzheimer's Disease (AD) Progression. By combining multi-view medical imaging data, attention gates, and clustering approaches, the system improves disease prediction accuracy. The suggested methodology overcomes the constraints of classic single-view approaches by using attention processes to focus on crucial regions of MRI scans and clustering algorithms to find common progression patterns among patients. This approach uses machine learning to increase segmentation and prediction skills, resulting in a scalable solution for neurodegenerative disease prediction. 3 Claims and 2 Figures

Specification

Description:Field of the Invention
The Attention U-Net Based Multi-View Clustering Model for Predicting Alzheimer's Disease Progression belongs to Medical Imaging and Neuro-informatics. It uses modern technologies like Deep Learning, Image Processing, Multi-View Data Integration, and Machine Learning Algorithms to analyse brain scans and clinical data to diagnose and forecast Alzheimer's disease progression.
Background of the Invention
Alzheimer's disease (AD) is a neurological condition that affects memory and cognitive function. Current Alzheimer's disease prediction models frequently rely on single-modality data, such as MRI scans, limiting their capacity to capture the illness's entire complexity. The diversity in AD progression from patient to patient makes reliable prediction challenging, emphasizing the need for models that incorporate multi-view and multi-modal data.
Traditional approaches struggle to interpret data from many perspectives and modalities, resulting in inefficiencies in diagnosis and treatment planning. There is an increasing demand for improved models that can effectively segment and categorize medical pictures, including data from MRIs, PET scans, and clinical information to provide a more comprehensive and trustworthy forecast of Alzheimer's progression.
The innovation disclosed in CN111488914B tackles these constraints by introducing a multi-task learning-based system for Alzheimer's disease classification and prediction. This approach uses medical imaging data and clinical indicators to improve prediction accuracy. This approach allows for the early detection of Alzheimer's disease by combining data from numerous sources, perhaps leading to more prompt interventions and personalized treatment strategies.
CN113658721A describes a method for forecasting Alzheimer's disease development utilizing a multi-view data fusion approach. By combining MRI, clinical, and molecular data, this approach seeks to deliver more precise and trustworthy predictions. The synthesis of data from diverse perspectives increases the model's ability to spot crucial trends in illness progression, allowing doctors to make more informed decisions. CN114638994B describes a multi-modal image categorization system built on an attention-enhanced U-Net architecture. This technique enhances feature extraction and classification accuracy by using attention processes to concentrate on crucial areas in multi-modal images. The invention provides an improved method for differentiating weakly discriminative characteristics, resulting in more accurate segmentation of Alzheimer s - related areas in the brain.
JP2012100019A describes a multi-viewpoint image encoding and decoding system to lessen the computational load required to analyse complicated medical imaging data. Encoding images from numerous angles allows for more efficient storage and retrieval of key imaging data, improving Alzheimer's progression tracking accuracy. JP2009100071A presents a method for decoding multi-view images with reduced redundancy, which addresses the inefficiencies of previous systems. It greatly enhances the speed and accuracy of multi-view image processing, making it perfect for tracking the evolution of disorders like Alzheimer's. KR101375666B1 presents a better method for encoding and decoding multi-view pictures using global disparity. This approach improves Alzheimer's diagnostic accuracy by delivering precise information about brain structures from several angles, decreasing the requirement for large amounts of computer resources while retaining excellent picture reconstruction accuracy.
US11101039B2 describes a machine-learning-based system that predicts Alzheimer's disease progression based on clinical data. This approach uses present and historical data to predict future trends in cognitive impairment, allowing for earlier intervention and more personalized treatment regimens. This idea uses powerful machine learning techniques to dramatically increase the accuracy of Alzheimer's progression predictions. US11462325B2 describes a multimodal machine learning-based clinical predictor that combines molecular and imaging data to provide a complete picture of Alzheimer's disease progression. This approach improves prediction accuracy by mixing gene expression data and biopsy pictures, allowing doctors to make better-educated decisions about therapy and care. US20220367053A1 describes a multimodal fusion system that integrates morphological data from histology with molecular information from omics to improve Alzheimer's diagnosis and prognosis. The method combines several data sets using deep learning, providing a more robust and comprehensive approach to forecasting Alzheimer's disease progression and patient response to therapy.
Summary of the Invention
The proposed innovation introduces an Attention U-Net-based Multi-View Clustering Model to enhance Alzheimer's Disease (AD) progression predictions. This model uses multi-view data, such as medical imaging and clinical information, to forecast the progression of AD more accurately. The model uses U-Net architecture and attention techniques to improve feature extraction from various views and layers of MRI images, resulting in more extensive and detailed analysis.
The model is intended to overcome the constraints of single-view data analysis in Alzheimer's progression prediction, as existing methods may overlook vital spatial and contextual information. Combining attention gates and multi-view clustering approaches, the system detects and prioritizes the most important regions in MRI scans for study. Furthermore, the multi-view strategy minimizes redundancy while improving the model's ability to collect weakly discriminative signals, critical for forecasting subtle changes in the brain as Alzheimer's disease advances.
The idea uses image segmentation, clustering algorithms, and machine learning to ensure that AD progression predictions are precise and efficient. Its ability to handle multi-view data helps overcome problems caused by variances in patient disease trajectories, making it an effective tool for both physicians and researchers.

Brief Description of Drawings
The invention will be described in detail with reference to the exemplary embodiments shown in the figures wherein:
Figure-1: Flow chart representing the work flow of the system
Figure-2: U-Net architecture diagram with attention gates and skip connections, optimizing multi-view clustering for Alzheimer's progression prediction.
Detailed Description of the Invention
The present invention involves predicting the progression of Alzheimer's Disease (AD) through multi-view medical imaging using the augmented architecture. Attention mechanisms can be applied to it in the prediction model that considers the U-Net. AD is one of those neurodegenerative diseases that progressively decreases the weakening of memories, and monitoring is often provided by using imaging. That is why the multi-view model suggested here, based on different algorithms of machine learning and gate attention, enables a highly accurate prediction related to Alzheimer's Disease's progressions.
According to (1), this work integrates multi-viewing angles of the brain data collected through imaging to increase feature extraction accuracy towards the prediction of development due to Alzheimer's. Initially, U-Net architectures were proposed for the specific task of biomedical image segmentation. It has two networks: an encoder and a decoder having skip connections that can capture both fine-grained features and global contextual information from input images. The encoder portion has convolutional layers that down-sample the spatial dimensions of a feature map, thus permitting abstraction while keeping salient features with the help of ReLU activations and max-pooling operations. The decoder component up-samples these feature maps into their original spatial dimensions to reconstruct a segmented output, marking key regions of AD progression. Critical spatial information is maintained by skip connections in the U-Net connecting corresponding layers in the encoder and decoder, respectively.
The improved U-Net in figure 2 uses attention gates (AGs), defined in (2), to increase the accuracy of segmentation of MRI images by selectively paying attention to critical regions.
These attention gates learn, dynamically how to pay more attention to significant characteristics. This focus guides the model towards the parts of the image that have the most significance. Such adaptation supports filtering of uninformative or non-relevant parts from view, such as extrinsic to the brain from relevance; it is indeed the major characteristic for early progression related to very subtle AD-associated structural changes that are required to be discovered by such attention gates that, similar to other possible scenarios for adaptation, allow adapting within complex imaging settings. This innovation is very characteristic of multi-view data integration: instead of using single-perspective data, a model processes multiple views from the brain, such as axial, coronal, and sagittal MRI images. This multi-view technology enables the system to recognize details that might be neglected with a single scan according to (1).
The invention makes use of clustering algorithms as outlined in (1) for improving the prediction of AD progression by grouping similar disease trajectories. The model, therefore, produces a more accurate prediction of each patient's disease path by clustering data from multiple views. Techniques like K-means, Hierarchical Clustering, and DBSCAN are used for the classification of patients into clusters with similar progression trends. These clusters aid the model in analyzing differences in the way Alzheimer's progresses in individual patients and facilitate a tailored predictive approach to each patient. Clustering is critical for reducing intra-class variance to ensure the robustness of predictions when applied to the heterogeneous population of patients.
The prediction part of the invention as described above hinges on more sophisticated machine learning techniques that are referred to in (2). After feature extraction and clustering, the model then uses machine learning algorithms for future AD progression prediction. The patients are classified into the respective categories of progression- either rapid, mild or no progression-using the data from MRI imaging as well as clinical history through the use of SVM. Also, Random Forest classifiers estimate the relative importance of features, such as the relative contribution of brain atrophy and cognitive test scores. This results in a finer-grained prediction based on risk factors. This approach combines both imaging and clinical data for higher diagnostic precision. The model is heavily cross-validated and tuned by reliability across diverse populations; grid search and hyperparameter optimization maximize performance.
The system also includes clinical and genetic data for a multidimensional analysis of AD, as mentioned in (2). To put predictions into a contextual framework, clinical test scores, such as the Mini-Mental State Examination, are taken into consideration; genetic factors, including the APOE ε4 allele, help to refine the estimates of disease progression. Biomarkers, including the levels of beta-amyloid and tau protein are integrated into the model, adding more complexity to the analysis. It is this integration of neuroimaging, clinical, and genetic data that makes the system possible to predict AD progression very accurately so that clinicians can assess several risk factors all at once.
This architecture, according to (3), automates feature selection on attention gates, meaning no human will be able to intervene in the whole process of analysis, hence bestowing an effective advantage in the model. The critical feature is then prioritized via processing and can handle greater datasets with minimal oversight in a human, achieving high throughput in clinical environments.
The versatility described for the system in (4) allows it to easily be adapted for other neurodegenerative diseases, as well as for various imaging modalities, thus making an adaptable diagnostic tool, that will be able to handle a wide range of medical settings. This adaptability could make it useful for more than diagnosis of Alzheimer's.
Then, (5) states that the model's capacity to handle multi-view and multi-modal data will further improve the outcome of diagnostics, thereby permitting a more holistic prediction of Alzheimer's and related diseases. This capacity to integrate multiple data types for analysis and clustering makes it a powerful tool for personalized and precise diagnosis in neurological applications.
3 Claims and 2 Figures
Equivalents
The present invention, a multi-view clustering model for Alzheimer's disease progression prediction based on Attention U-Net, uses clinical, genetic, and imaging data to improve diagnosis accuracy. The model can be applied to different imaging modalities and modified for other neurodegenerative diseases, therefore the invention's reach is not just restricted to Alzheimer's disease. Because of its adaptability, it can be used as a diagnostic tool for complete neuroimaging and predictive analytics in a variety of medical scenarios, not just AD. , Claims:The scope of the invention is defined by the following claims:

Claim:
1. The attention u-net based multi-view clustering model for predicting alzheimer's disease progression comprising,
a) The system integrates multi-view imaging data from various angles of the brain to improve feature extraction and prediction accuracy for Alzheimer's disease development. It employs a modified U-Net architecture with attention gates to selectively highlight critical brain regions while reducing irrelevant input processing.
b) The attention gates improve segmentation accuracy by focusing on key brain areas. Multi-view clustering classifies people based on comparable sickness patterns, which improves personalized forecasting.
c) The invention enhances prediction accuracy by integrating MRI data with clinical and genetic information, such as cognitive test scores and APOE ε4 markers.
2. According to claim 1, the machine learning algorithms, such as Support Vector Machines and Random Forest classifiers, combine imaging and clinical data to forecast Alzheimer's disease development. The program can analyse longitudinal data, assisting in forecasting brain changes over time and determining the rate of disease progression.
3. As per claim 1, the model's attention gates automate the feature selection process, avoiding user involvement and increasing overall system efficiency. The technique prioritises important traits during data processing. This automation boosts the model's ability to handle larger datasets.

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