Mixed prototype correction for causal inference in medical image classification.

Area of Science:

• Medical imaging and artificial intelligence
• Causal inference in machine learning
• Biomedical data analysis

Background:

• Medical image heterogeneity presents significant challenges for accurate disease diagnosis.
• Current deep learning models often overlook the impact of heterogeneity on causal relationships between image features and diagnostic labels.
• Addressing unseen confounding factors is crucial for reliable medical image diagnosis.

Purpose of the Study:

• To propose a novel method, mixed prototype correction for causal inference (MPCCI), to mitigate the impact of confounding factors in medical images.
• To enhance the diagnostic accuracy and reliability of deep learning models in the presence of medical image heterogeneity.
• To incorporate causal inference principles into deep learning model design for medical image analysis.

Main Methods:

• The proposed MPCCI method integrates a causal inference component using front-door adjustment with an adaptive training strategy.
• A multi-view feature extraction (MVFE) module establishes mediators, while a mixed prototype correction (MPC) module performs causal interventions.
• The adaptive training strategy utilizes information purity and maturity metrics for stable model training.

Main Results:

• Experimental evaluations on four diverse medical image datasets (CT and ultrasound) demonstrated the effectiveness of the MPCCI method.
• The MPCCI approach significantly improved diagnostic accuracy compared to existing methods.
• The proposed method showed superior reliability in handling heterogeneous medical image data.

Conclusions:

• The MPCCI method offers a robust solution for enhancing deep learning-based medical image diagnosis by accounting for heterogeneity and confounding factors.
• Integrating causal inference into deep learning models is a promising direction for improving medical diagnostic systems.
• The developed method has the potential to advance the reliability and accuracy of AI-driven diagnostic tools in healthcare.