Alzheimer's Prediction Model Training via Segmented Modality Standardization
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Solution Overview
Problem
Insufficient and inadequate training data poses a challenge in establishing stable and accurate prediction models, particularly in machine learning and artificial intelligence applications, where the completeness and correctness of training data are crucial for reliable predictions.
Innovation Solution
A method is developed to establish prediction models by obtaining pre-established single-modality and multi-modalities standard models, using a combination of single-modality and multi-modalities training data, and employing a teacher-student model training architecture to generate reliable prediction results for input data with different modality combinations, specifically for Alzheimer's disease prognosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional model training methods are used with insufficient training data, then the model training process is simple, but the model credibility and prediction accuracy deteriorate
Solution Approach 1:
The training process is segmented into multiple stages: first training single-modality standard models for each modality type, then training multi-modality standard models by combining different modality combinations, and finally training the prediction model using both single and multi-modality training data. This segmentation allows the model to learn from data systematically and improves credibility without requiring all data to be available simultaneously.
Solution Approach 2:
Pre-established standard models for single modality and multi-modalities are trained beforehand as teacher models. These standard models serve as guidance for training the prediction model, allowing the prediction model to learn from high-quality pre-trained models even when complete training data is insufficient, thereby improving model credibility.
2Adaptability or versatility
If only single-modality training data is used, then the training process is simple, but the model cannot handle multi-modality combinations effectively
Solution Approach 1:
The training data is segmented into single-modality samples and multi-modality samples with different combinations. The system separately processes these segments through different training stages, first establishing single-modality standard models and then multi-modality standard models, before integrating them into the final prediction model. This segmentation enables the model to handle various modality combinations effectively.
Solution Approach 2:
The prediction model is designed to be universal and can handle multiple modality combinations (single-modality, multi-modality, and complete-modality data) through the same model architecture. The multi-modality standard models serve as teacher models that guide the prediction model to learn from different modality combinations, making the prediction model versatile and adaptable to various input types.
3Quantity of substance
If complete-modality training data is required, then the training data quality is high, but the availability of training data becomes insufficient
Solution Approach 1:
The training data is segmented by modality completeness into single-modality samples, multi-modality samples with different combinations, and complete-modality samples. The system trains standard models at different levels (single-modality standard models, multi-modality standard models) and uses these segmented data at appropriate training stages. This segmentation allows the system to utilize available data effectively even when complete-modality data is insufficient.
Solution Approach 2:
Standard models are trained preliminarily using available single-modality and multi-modality data before training the final prediction model. These pre-established standard models serve as teacher models that guide the prediction model training, allowing the system to proceed with training even when complete-modality data is insufficient, thereby maximizing the utilization of available training data.
Data Source
AI summary
A method for establishing robust prediction model is adapted for solving the problem that the conventional prediction model cannot generate stable and credible results with missing data. The method of the present invention includes the following steps: obtaining pre-established single-modality standard models respectively based on each type of modalities from samples; extracting modality sets each having the same modality types from the samples to establish corresponding multi-modalities standard models; extracting multiple combinations of the modality sets from the samples having complete modalities to be training data, wherein the multiple combinations of the modality sets can be classified into single-modality, multi-modalities and complete-modalities; inputting said training data into a to-be trained prediction model, and modifying the prediction model by said single-modality standard models and said multi-modalities standard models to obtain a well-trained prediction model.