Multi-Magnification Image Search in Digital Pathology


This study proposes the use of multi-magnification image representation and investigates the effect that magnification has on content-based image search within digital pathology archives. Researchers and other medical professionals now have the ability, thanks to image search within large archives of digital pathology slides, to match patient records from current patients with those of patients from the past and to gain knowledge from cases that have been diagnosed and treated. Pathologists use microscopes and change the magnification level as they look at tissue samples in order to identify and evaluate a wide variety of morphological characteristics. We were motivated to investigate various magnification levels in digital pathology by the workflow of traditional pathology. Our goal was to narrow the gap between AI-enabled image search methods and clinical environments by studying these magnification levels and their combinations. The search framework that has been proposed does not rely on any kind of regional annotation, and it has the potential to be applied to millions of whole slide images that are not labeled. This study examines the effectiveness of two different methods for combining different magnification levels and offers recommendations for both. The first method produces a deep feature representation for a digital slide that is composed of a single vector, whereas the second method makes use of a deep feature representation that is composed of multiple vectors. We report the search results of a subset of The Cancer Genome Atlas (TCGA) repository using magnifications of 20, 10, and 5 and their combinations. The experiments provide evidence that looking at information at the cellular level with the greatest possible magnification is necessary when searching for diagnostic purposes. In contrast, information obtained at a low magnification might improve this assessment depending on the kind of tumor being examined. When compared to the single-magnification image search, our multi-magnification approach was able to achieve an improvement in F1-score of up to 11% when searching among the brain tumor and urinary tract tumor subtypes.

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