arXiv · · Significant research
Researchers at MBZUAI have introduced TiBiX, a novel approach leveraging temporal information from previous chest X-rays (CXRs) and reports for bidirectional generation of current CXRs and reports. TiBiX addresses two key challenges: generating current images from previous images and reports, and generating current reports from both previous and current images. The study also introduces a curated temporal benchmark dataset derived from the MIMIC-CXR dataset and achieves state-of-the-art results in report generation.
Chest X-ray · CXR · Report Generation · Temporal Information · MIMIC-CXR
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arXiv ·
MBZUAI researchers introduce XrayGPT, a conversational medical vision-language model for analyzing chest radiographs and answering open-ended questions. The model aligns a medical visual encoder (MedClip) with a fine-tuned large language model (Vicuna) using a linear transformation. To enhance performance, the LLM was fine-tuned using 217k interactive summaries generated from radiology reports.
arXiv ·
The paper introduces MedPromptX, a clinical decision support system using multimodal large language models (MLLMs), few-shot prompting (FP), and visual grounding (VG) for chest X-ray diagnosis, integrating imagery with EHR data. MedPromptX refines few-shot data dynamically for real-time adjustment to new patient scenarios and narrows the search area in X-ray images. The study introduces MedPromptX-VQA, a new visual question answering dataset, and demonstrates state-of-the-art performance with an 11% improvement in F1-score compared to baselines.
arXiv ·
Researchers from MBZUAI have developed XReal, a diffusion model for generating realistic chest X-ray images with precise control over anatomy and pathology location. The model utilizes an Anatomy Controller and a Pathology Controller to introduce spatial control in a pre-trained Text-to-Image Diffusion Model without fine-tuning. XReal outperforms existing X-ray diffusion models in realism, as evaluated by quantitative metrics and radiologists' ratings, and the code/weights are available.
arXiv ·
MBZUAI releases BiMediX2, a bilingual (Arabic-English) Bio-Medical Large Multimodal Model, along with the BiMed-V dataset (1.6M samples) and BiMed-MBench evaluation benchmark. BiMediX2 supports multi-turn conversation in Arabic and English and handles diverse medical imaging modalities. The model achieves state-of-the-art results on medical LLM and LMM benchmarks, outperforming existing methods and GPT-4 in specific evaluations.