A wide range of imaging techniques and data formats available for medical images make accurate retrieval from image databases challenging. Efficient retrieval systems are crucial in advancing medical research, enabling large-scale studies and innovative diagnostic tools. Thus, addressing the challenges of medical image retrieval is essential for the continued enhancement of healthcare and research. In this study, we evaluated the feasibility of employing four state-of-the-art pretrained models for medical image retrieval at modality, body region, and organ levels and compared the results of two similarity indexing approaches. Since the employed networks take 2D images, we analyzed the impacts of weighting and sampling strategies to incorporate 3D information during retrieval of 3D volumes. We showed that medical image retrieval is feasible using pretrained networks without any additional training or fine-tuning steps. Using pretrained embeddings, we achieved a recall of 1 for various tasks at modality, body region, and organ level.

翻译：广泛的成像技术和数据格式给医学图像数据库的精确检索带来了挑战。高效的检索系统对于推进医学研究、实现大规模研究和创新诊断工具至关重要。因此，应对医学图像检索中的挑战对于持续提升医疗保健和科研水平具有重大意义。本研究评估了四种先进预训练模型在模态、身体部位及器官层面进行医学图像检索的可行性，并比较了两种相似性索引方法的结果。由于所用网络处理二维图像，我们分析了在三维体素检索过程中融入三维信息的加权策略和采样策略的影响。研究证明，无需额外训练或微调步骤，即可利用预训练网络实现医学图像检索。采用预训练嵌入，我们在模态、身体部位及器官层面的多项任务中均实现了1.0的召回率。