DiA-gnostic VLVAE: Disentangled Alignment-Constrained Vision Language Variational AutoEncoder for Robust Radiology Reporting with Missing Modalities

• URL: http://arxiv.org/abs/2511.05968v1
• Date: Sat, 08 Nov 2025 11:08:27 GMT
• Title: DiA-gnostic VLVAE: Disentangled Alignment-Constrained Vision Language Variational AutoEncoder for Robust Radiology Reporting with Missing Modalities
• Authors: Nagur Shareef Shaik, Teja Krishna Cherukuri, Adnan Masood, Dong Hye Ye,
• Abstract summary: We propose the DiA-gnostic VLVAE, which achieves robust radiology reporting through Disentangled Alignment.<n>Our framework is designed to be resilient to missing modalities by disentangling shared and modality-specific features.<n>A compact LLaMA-X decoder then uses these disentangled representations to generate reports efficiently.
• Score: 3.5045368873011924
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The integration of medical images with clinical context is essential for generating accurate and clinically interpretable radiology reports. However, current automated methods often rely on resource-heavy Large Language Models (LLMs) or static knowledge graphs and struggle with two fundamental challenges in real-world clinical data: (1) missing modalities, such as incomplete clinical context , and (2) feature entanglement, where mixed modality-specific and shared information leads to suboptimal fusion and clinically unfaithful hallucinated findings. To address these challenges, we propose the DiA-gnostic VLVAE, which achieves robust radiology reporting through Disentangled Alignment. Our framework is designed to be resilient to missing modalities by disentangling shared and modality-specific features using a Mixture-of-Experts (MoE) based Vision-Language Variational Autoencoder (VLVAE). A constrained optimization objective enforces orthogonality and alignment between these latent representations to prevent suboptimal fusion. A compact LLaMA-X decoder then uses these disentangled representations to generate reports efficiently. On the IU X-Ray and MIMIC-CXR datasets, DiA has achieved competetive BLEU@4 scores of 0.266 and 0.134, respectively. Experimental results show that the proposed method significantly outperforms state-of-the-art models.

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