Disease Classifier Construction Using Transfer Learning on Bio-signals
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Solution Overview
Problem
The development of disease classifiers for bio-signals faces challenges due to limited labeled medical data and the requirement for domain knowledge to identify representative features, making it difficult to achieve high disease-detection accuracy.
Innovation Solution
The method involves constructing a codebook of representative features from disease-irrelevant data using transfer representation learning, extracting transfer-learned disease features from bio-signals, and performing supervised learning to train a classifier for disease detection, leveraging techniques like deep learning and neural networks to encode bio-signals without relying on extensive medical domain knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a data-driven approach is used to learn fundamental features from bio-signals, then the classification accuracy can be improved, but a substantial amount of labeled training data is required which is laborious and expensive to obtain
Solution Approach 1:
The patent applies preliminary action by pre-training a neural network model on large volumes of unlabeled bio-signal data to learn fundamental time-series features before the actual disease classification task. This pre-training phase prepares the model with general temporal patterns, so that when labeled data becomes available, the model only needs fine-tuning rather than training from scratch, significantly reducing the amount of labeled data needed.
Solution Approach 2:
The patent introduces an intermediary mechanism by using a two-stage training process where unlabeled data serves as an intermediate training resource. The model first learns from abundant unlabeled data (intermediate step), then transfers this knowledge to the specific disease classification task using limited labeled data. This intermediary training phase bridges the gap between having no labeled data and needing accurate classification.
2Adaptability or versatility
If a model-based approach is used to encode all knowledge in a model, then domain expertise can be utilized, but the approach cannot work effectively when fundamental features of all abnormalities cannot be comprehensively enumerated
Solution Approach 1:
The patent applies self-service by enabling the neural network model to automatically learn and identify disease-relevant features from data without requiring explicit programming of domain knowledge. The model serves itself by adapting to different disease types and bio-signal characteristics through training, rather than relying on pre-encoded expert rules that may not cover all abnormalities.
Solution Approach 2:
The patent utilizes parameter changes by adjusting the neural network's learning parameters and architecture to adapt to different disease detection tasks. The model can modify its internal representations and feature extraction capabilities based on the specific bio-signal type and disease characteristics, providing both adaptability to domain expertise and reliability in detection accuracy.
Data Source
AI summary
The disclosure provides a method, an electronic apparatus, and a computer readable medium of constructing a classifier for disease detection. The method includes the following steps. A codebook of representative features is constructed based on a plurality of disease-irrelevant data. Transfer-learned disease features are extracted from disease-relevant bio-signals according to the codebook without any medical domain knowledge, where both the disease-irrelevant data and the disease-relevant bio-signals are time-series data. Supervised learning is performed based on the transfer-learned disease features to train the classifier for disease detection.