Interpretable thoracic pathologic prediction via learning group-disentangled representation | JoVE Visualize

Area of Science:

Artificial Intelligence
Medical Imaging
Computer Vision

Background:

Deep learning models achieve high performance in medical image analysis but often lack interpretability, posing risks for accurate diagnosis.
The 'black box' nature of deep learning hinders clinical integration due to the inability to understand decision-making processes.
Integrating clinical knowledge and ensuring human interpretability are crucial for reliable AI-driven medical diagnostics.

Purpose of the Study:

To develop an interpretable deep learning framework, the Representation Group-Disentangling Network (RGD-Net), for medical image analysis.
To disentangle feature representations in X-ray images, linking specific feature groups to distinct disease diagnoses.
To enhance the clinical utility of deep learning by providing explainable insights into diagnostic predictions.

Main Methods:

Proposed the Representation Group-Disentangling Network (RGD-Net) utilizing an auto-encoder structure for interpretable prediction.
Introduced a Group-Disentangle Module to extract group-disentangled representations, creating a semantic latent space through attribute consistency.
Implemented adversarial constraints to prevent model collapse and ensure robust feature-to-disease mapping.

Main Results:

RGD-Net successfully disentangled feature spaces of X-ray images into independent groups, each contributing to specific disease diagnoses.
Experiments on public datasets demonstrated RGD-Net's superiority over comparative methods in leveraging disease-specific factors.
The proposed network aids clinicians by providing interpretable insights, facilitating more reasonable diagnoses.

Conclusions:

RGD-Net significantly enhances interpretability in deep learning for medical image analysis by disentangling features.
The framework offers a novel approach to embed clinical knowledge and correct biases in AI diagnostic models.
This work paves the way for more trustworthy and clinically applicable deep learning solutions in medical imaging.