Variational deep embedding-based active learning for the diagnosis of pneumonia

Area of Science:

• Artificial Intelligence
• Medical Informatics
• Machine Learning

Background:

• Machine learning models improve accuracy through iterative training, similar to human learning.
• Accurate pneumonia diagnosis is crucial for effective patient treatment and management.
• Current active learning (AL) methods can be enhanced for greater label efficiency.

Purpose of the Study:

• To introduce a novel human-centric active learning framework, variational deep embedding-based active learning (VaDEAL).
• To improve the accuracy and efficiency of pneumonia diagnosis using machine learning.
• To develop an advanced AL strategy incorporating clustering for superior sample selection.

Main Methods:

• The VaDEAL framework integrates a variational deep embedding (VaDE) module, a task learner, and a sampling calculator.
• VaDE performs unsupervised dimensionality reduction and clustering.
• The sampling calculator assesses sample uncertainty, representativeness, and diversity for informative data selection.

Main Results:

• The proposed VaDEAL framework demonstrated superior performance compared to state-of-the-art methods in pneumonia diagnosis.
• Extensive experiments on a large dataset confirmed the high accuracy of the VaDEAL approach.
• The combination of uncertainty, representativeness, and diversity scores effectively identified the most valuable samples for labeling.

Conclusions:

• VaDEAL offers a significant advancement in label-efficient learning for medical diagnosis.
• The framework enhances pneumonia diagnostic accuracy by intelligently selecting training data.
• VaDEAL represents a promising human-centric computing approach for improving healthcare outcomes.