CheXLearner: Text-Guided Fine-Grained Representation Learning for Progression Detection

• URL: http://arxiv.org/abs/2505.06903v1
• Date: Sun, 11 May 2025 08:51:38 GMT
• Title: CheXLearner: Text-Guided Fine-Grained Representation Learning for Progression Detection
• Authors: Yuanzhuo Wang, Junwen Duan, Xinyu Li, Jianxin Wang,
• Abstract summary: We present CheXLearner, the first end-to-end framework that unifies anatomical region detection, structure alignment, and semantic guidance.<n>Our proposed Med-Manifold Alignment Module (Med-MAM) leverages hyperbolic geometry to robustly align anatomical structures.<n>Our model attains a 91.52% average AUC score in downstream disease classification, validating its superior feature representation.
• Score: 14.414457048968439
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Temporal medical image analysis is essential for clinical decision-making, yet existing methods either align images and text at a coarse level - causing potential semantic mismatches - or depend solely on visual information, lacking medical semantic integration. We present CheXLearner, the first end-to-end framework that unifies anatomical region detection, Riemannian manifold-based structure alignment, and fine-grained regional semantic guidance. Our proposed Med-Manifold Alignment Module (Med-MAM) leverages hyperbolic geometry to robustly align anatomical structures and capture pathologically meaningful discrepancies across temporal chest X-rays. By introducing regional progression descriptions as supervision, CheXLearner achieves enhanced cross-modal representation learning and supports dynamic low-level feature optimization. Experiments show that CheXLearner achieves 81.12% (+17.2%) average accuracy and 80.32% (+11.05%) F1-score on anatomical region progression detection - substantially outperforming state-of-the-art baselines, especially in structurally complex regions. Additionally, our model attains a 91.52% average AUC score in downstream disease classification, validating its superior feature representation.

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