GraphSKT: Graph-Guided Structured Knowledge Transfer for Domain Adaptive Lesion Detection.

Area of Science:

• Medical image analysis
• Computer vision
• Machine learning

Background:

• Domain adaptive detection methods often fail to capture image topology, leading to poor performance with medical imaging variations.
• Distributional shift in medical images, like geometric distortions, hinders effective knowledge transfer.
• Adversarial adaptation can cause negative transfer when intrinsic image structures are ignored.

Purpose of the Study:

• To propose a novel framework for domain adaptive lesion detection using relational reasoning.
• To address the limitations of current methods in handling topological structures and distributional shifts in medical images.
• To enhance knowledge transfer by modeling intra- and inter-domain topological structures.

Main Methods:

• Developed a Graph-Structured Knowledge Transfer (GraphSKT) framework.
• Modeled intra- and inter-domain topological structures using graph representations.
• Utilized cross-domain correspondence to identify foreground regions for graph nodes.
• Propagated contextual and semantic information through graph nodes for feature enhancement.

Main Results:

• GraphSKT significantly outperforms state-of-the-art approaches on challenging datasets.
• Demonstrated superior performance in detecting polyps in colonoscopy images.
• Achieved high accuracy in detecting masses in mammographic images.
• The framework effectively transfers structured knowledge across domains.

Conclusions:

• Graph-Structured Knowledge Transfer (GraphSKT) offers a robust approach for domain adaptive lesion detection.
• Modeling topological structures is crucial for accurate knowledge transfer in medical imaging.
• The proposed method enhances feature expressiveness for improved lesion detection accuracy.