![]() A critical requirement for making data findable, accessible, interoperable, and reusable (FAIR) and for enabling AI is the availability of standardized metadata and standard application programming interfaces (APIs) 2, 7, 8, 9, 10, 11, 12. Given their immense size, slide microscopy imaging data sets generally exceed the capacity of local storage and are hence stored on remote servers and accessed over a network. Unfortunately, sharing of imaging data and reporting of imaging findings are lagging with detrimental effects on the reproducibility of microscopy imaging science 5 and on the clinical adoption of computational pathology 6. Slide microscopy imaging data acquired in biomedical research and clinical pathology practice are increasing in size, dimensionality, and complexity 1 and technological advances in machine learning (ML) and artificial intelligence (AI) promise to exploit large and rich imaging data to drive discoveries and to support humans in data interpretation and in data-informed decision making 2, 3, 4. We further show how Slim enables the collection of standardized image annotations for the development or validation of machine learning models and the visual interpretation of model inference results in the form of segmentation masks, spatial heat maps, or image-derived measurements. We showcase the capabilities of Slim as the slide microscopy viewer of the NCI Imaging Data Commons and demonstrate how the viewer enables interactive visualization of traditional brightfield microscopy and highly-multiplexed immunofluorescence microscopy images from The Cancer Genome Atlas and Human Tissue Atlas Network, respectively, using standard DICOMweb services. We introduce Slim, an open-source, web-based slide microscopy viewer that implements the internationally accepted Digital Imaging and Communications in Medicine (DICOM) standard to achieve interoperability with a multitude of existing medical imaging systems. ![]() ![]() The exchange of large and complex slide microscopy imaging data in biomedical research and pathology practice is impeded by a lack of data standardization and interoperability, which is detrimental to the reproducibility of scientific findings and clinical integration of technological innovations.
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