Medical Image Retrieval
7 papers with code • 1 benchmarks • 3 datasets
This paper describes the field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening.
This paper proposes to generate and to use barcodes to annotate medical images and/or their regions of interest such as organs, tumors and tissue types.
The triplet cross-entropy loss can help to map the classification information of images and similarity between images into the hash codes.
Overall, the results of image retrieval in breast cancer applying the CNN based Autoencoder method achieved higher performance compared to the method used in the previous study with an average precision of 0. 9237 in the mainclass dataset category and 0. 6825 in the subclass dataset category.
We extensively tested the proposed NNS approach and compared the performance with state-of-the-art NNS approaches on benchmark datasets and our created medical image datasets.