NEURAL: Attention-Guided Pruning for Unified Multimodal Resource-Constrained Clinical Evaluation

• URL: http://arxiv.org/abs/2508.09715v1
• Date: Wed, 13 Aug 2025 11:08:09 GMT
• Title: NEURAL: Attention-Guided Pruning for Unified Multimodal Resource-Constrained Clinical Evaluation
• Authors: Devvrat Joshi, Islem Rekik,
• Abstract summary: NEURAL is a novel framework that addresses the storage and transmission challenges of medical imaging data.<n>Our approach repurposes cross-attention scores between the image and its radiological report to structurally prune chest X-rays.<n>NEURAL achieves a 93.4-97.7% reduction in image data size while maintaining a high diagnostic performance of 0.88-0.95 AUC.
• Score: 6.253771639590563
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The rapid growth of multimodal medical imaging data presents significant storage and transmission challenges, particularly in resource-constrained clinical settings. We propose NEURAL, a novel framework that addresses this by using semantics-guided data compression. Our approach repurposes cross-attention scores between the image and its radiological report from a fine-tuned generative vision-language model to structurally prune chest X-rays, preserving only diagnostically critical regions. This process transforms the image into a highly compressed, graph representation. This unified graph-based representation fuses the pruned visual graph with a knowledge graph derived from the clinical report, creating a universal data structure that simplifies downstream modeling. Validated on the MIMIC-CXR and CheXpert Plus dataset for pneumonia detection, NEURAL achieves a 93.4-97.7\% reduction in image data size while maintaining a high diagnostic performance of 0.88-0.95 AUC, outperforming other baseline models that use uncompressed data. By creating a persistent, task-agnostic data asset, NEURAL resolves the trade-off between data size and clinical utility, enabling efficient workflows and teleradiology without sacrificing performance. Our NEURAL code is available at https://github.com/basiralab/NEURAL.

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