  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. Slim is 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. 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 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.
