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.

本研究中，我们评估了使用四种最先进的预训练模型在模态、身体区域和器官级别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。结果表明，在没有额外训练或微调步骤的情况下，使用预训练网络进行医学图像检索是可行的，并且在模态、身体区域和器官级别的各种任务中实现了召回率为 1。