ARReST reduces WSI patch indexes by 14% on average (range 3-60%) on TCGA data across 21 organs by removing antithetical patches while preserving retrieval performance.
MinMax Radon Barcodes for Medical Image Retrieval
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Content-based medical image retrieval can support diagnostic decisions by clinical experts. Examining similar images may provide clues to the expert to remove uncertainties in his/her final diagnosis. Beyond conventional feature descriptors, binary features in different ways have been recently proposed to encode the image content. A recent proposal is "Radon barcodes" that employ binarized Radon projections to tag/annotate medical images with content-based binary vectors, called barcodes. In this paper, MinMax Radon barcodes are introduced which are superior to "local thresholding" scheme suggested in the literature. Using IRMA dataset with 14,410 x-ray images from 193 different classes, the advantage of using MinMax Radon barcodes over \emph{thresholded} Radon barcodes are demonstrated. The retrieval error for direct search drops by more than 15\%. As well, SURF, as a well-established non-binary approach, and BRISK, as a recent binary method are examined to compare their results with MinMax Radon barcodes when retrieving images from IRMA dataset. The results demonstrate that MinMax Radon barcodes are faster and more accurate when applied on IRMA images.
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Benchmarking on TCGA shows TITAN foundation model edges out others for whole-slide retrieval but with only ~68% average accuracy, high organ-to-organ variation, and no consistent winner over patch-level baselines.
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Reducing Redundancy in Whole-Slide Image Patching for Scalable Indexing and Retrieval
ARReST reduces WSI patch indexes by 14% on average (range 3-60%) on TCGA data across 21 organs by removing antithetical patches while preserving retrieval performance.
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Validation of Whole-Slide Foundation Models for Image Retrieval in TCGA Data
Benchmarking on TCGA shows TITAN foundation model edges out others for whole-slide retrieval but with only ~68% average accuracy, high organ-to-organ variation, and no consistent winner over patch-level baselines.