FGML-DG: Feynman-Inspired Cognitive Science Paradigm for Cross-Domain Medical Image Segmentation

2026-04-12

AI Summary

This work addresses the challenge of domain generalization in cross-modal and heterogeneous medical image segmentation, where domain shift, imaging discrepancies, and patient variability lead to significant performance degradation on unseen domains. Inspired by the Feynman learning technique, the authors propose the first cognitive science–driven meta-learning framework featuring three key innovations: concept-simplification–based style statistical alignment, a MetaStyle meta-memory mechanism enabling cross-domain knowledge reuse, and feedback-driven retraining (FDRT) to optimize the learning process. The proposed method substantially outperforms existing domain generalization approaches on two challenging cross-domain medical image segmentation benchmarks.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Multi-modal Vision
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

In medical image segmentation across multiple modalities (e.g., MRI, CT, etc.) and heterogeneous data sources (e.g., different hospitals and devices), Domain Generalization (DG) remains a critical challenge in AI-driven healthcare. This challenge primarily arises from domain shifts, imaging variations, and patient diversity, which often lead to degraded model performance in unseen domains. To address these limitations, we identify key issues in existing methods, including insufficient simplification of complex style features, inadequate reuse of domain knowledge, and a lack of feedback-driven optimization. To tackle these problems, inspired by Feynman's learning techniques in educational psychology, this paper introduces a cognitive science-inspired meta-learning paradigm for medical image domain generalization segmentation. We propose, for the first time, a cognitive-inspired Feynman-Guided Meta-Learning framework for medical image domain generalization segmentation (FGML-DG), which mimics human cognitive learning processes to enhance model learning and knowledge transfer. Specifically, we first leverage the 'concept understanding' principle from Feynman's learning method to simplify complex features across domains into style information statistics, achieving precise style feature alignment. Second, we design a meta-style memory and recall method (MetaStyle) to emulate the human memory system's utilization of past knowledge. Finally, we incorporate a Feedback-Driven Re-Training strategy (FDRT), which mimics Feynman's emphasis on targeted relearning, enabling the model to dynamically adjust learning focus based on prediction errors. Experimental results demonstrate that our method outperforms other existing domain generalization approaches on two challenging medical image domain generalization tasks.

Problem

• Domain Generalization
• Medical Image Segmentation
• Domain Shift
• Cross-Domain
• Heterogeneous Data

Innovation

• Feynman-inspired learning
• Domain Generalization
• Medical Image Segmentation
• Meta-learning
• Cognitive Science