MEDIMP：基于有限表格数据生成临床提示的3D医学图像用于肾移植研究
MEDIMP: 3D Medical Images with clinical Prompts from limited tabular data for renal transplantation
AI总结：
针对肾移植预后中高维影像数据难提取有效生物标志物、多模态数据有限的问题，提出MEDIMP模型，将临床数据转文本提示后结合对比学习学习DCE MRI表征，实验验证其在有限数据下的临床有效性。
摘要：
肾移植是终末期肾病最有效的治疗方案。由于病因复杂，移植后慢性功能障碍的风险仍然很高，甚至可能导致移植物失功。在临床实践中，医学成像在肾移植监测中发挥着重要作用。然而，移植物监测涉及多学科协作，尤其是肾病学、泌尿学和放射学的结合，而从这类高维复杂数据中识别可靠的生物标志物用于预后预测颇具挑战。\n本研究受大语言模型（LLMs）近期成功的启发，提出MEDIMP——Medical Images with clinical Prompts（带临床提示的医学图像）模型，该模型将结构化临床生物学数据转化为文本提示后融入其中，用于学习肾移植动态对比增强磁共振成像（DCE MRI）的有效多模态表征。MEDIMP基于联合文本-图像配对嵌入的对比学习来完成这一艰巨任务。此外，我们还提出了一个框架，利用大语言模型的自动文本数据增强来生成医学提示。我们的目标是学习肾移植DCE MRI的有效流形，为移植或患者状态（移植后2年、3年和4年）的预后预测提供支持，同时最高效地充分利用有限的可用多模态数据。\n大量实验以及与其他有限数据下的肾移植表征学习方法的对比证明，MEDIMP在相关临床场景中具有有效性，为医学提示研究提供了新方向。我们的代码已发布于https://github.com/leomlck/MEDIMP。
英文摘要：
Renal transplantation emerges as the most effective solution for end-stage renal disease. Occurring from complex causes, a substantial risk of transplant chronic dysfunction persists and may lead to graft loss. Medical imaging plays a substantial role in renal transplant monitoring in clinical practice. However, graft supervision is multi-disciplinary, notably joining nephrology, urology, and radiology, while identifying robust biomarkers from such high-dimensional and complex data for prognosis is challenging. In this work, taking inspiration from the recent success of Large Language Models (LLMs), we propose MEDIMP -- Medical Images with clinical Prompts -- a model to learn meaningful multi-modal representations of renal transplant Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE MRI) by incorporating structural clinicobiological data after translating them into text prompts. MEDIMP is based on contrastive learning from joint text-image paired embeddings to perform this challenging task. Moreover, we propose a framework that generates medical prompts using automatic textual data augmentations from LLMs. Our goal is to learn meaningful manifolds of renal transplant DCE MRI, interesting for the prognosis of the transplant or patient status (2, 3, and 4 years after the transplant), fully exploiting the limited available multi-modal data most efficiently. Extensive experiments and comparisons with other renal transplant representation learning methods with limited data prove the effectiveness of MEDIMP in a relevant clinical setting, giving new directions toward medical prompts. Our code is available at https://github.com/leomlck/MEDIMP.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
