Medical Image Retrieval Using Pretrained Embeddings
This addresses the problem of efficient retrieval from diverse medical image databases for healthcare and research, but it is incremental as it applies existing methods to a new domain.
The study tackled the challenge of medical image retrieval by evaluating four pretrained models and two similarity indexing approaches, achieving perfect recall (1.0) for tasks at modality, body region, and organ levels without additional training.
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.