MicarVLMoE: A Modern Gated Cross-Aligned Vision-Language Mixture of Experts Model for Medical Image Captioning and Report Generation

• URL: http://arxiv.org/abs/2504.20343v1
• Date: Tue, 29 Apr 2025 01:26:02 GMT
• Title: MicarVLMoE: A Modern Gated Cross-Aligned Vision-Language Mixture of Experts Model for Medical Image Captioning and Report Generation
• Authors: Amaan Izhar, Nurul Japar, Norisma Idris, Ting Dang,
• Abstract summary: Medical image reporting aims to generate structured clinical descriptions from radiological images.<n>We propose MicarVLMoE, a vision-language mixture-of-experts model with gated cross-aligned fusion.<n>We extend MIR to CT scans, retinal imaging, MRI scans, and gross pathology images, reporting state-of-the-art results.
• Score: 4.760537994346813
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical image reporting (MIR) aims to generate structured clinical descriptions from radiological images. Existing methods struggle with fine-grained feature extraction, multimodal alignment, and generalization across diverse imaging types, often relying on vanilla transformers and focusing primarily on chest X-rays. We propose MicarVLMoE, a vision-language mixture-of-experts model with gated cross-aligned fusion, designed to address these limitations. Our architecture includes: (i) a multiscale vision encoder (MSVE) for capturing anatomical details at varying resolutions, (ii) a multihead dual-branch latent attention (MDLA) module for vision-language alignment through latent bottleneck representations, and (iii) a modulated mixture-of-experts (MoE) decoder for adaptive expert specialization. We extend MIR to CT scans, retinal imaging, MRI scans, and gross pathology images, reporting state-of-the-art results on COVCTR, MMR, PGROSS, and ROCO datasets. Extensive experiments and ablations confirm improved clinical accuracy, cross-modal alignment, and model interpretability. Code is available at https://github.com/AI-14/micar-vl-moe.

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