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Machine learning is revolutionizing image-based diagnostics in pathology andradiology. ML models have shown promising results in research settings, buttheir lack of interoperability has been a major barrier for clinicalintegration and evaluation. The DICOM a standard specifies Information ObjectDefinitions and Services for the representation and communication of digitalimages and related information, including image-derived annotations andanalysis results. However, the complexity of the standard represents anobstacle for its adoption in the ML community and creates a need for softwarelibraries and tools that simplify working with data sets in DICOM format. Herewe present the highdicom library, which provides a high-level applicationprogramming interface for the Python programming language that abstractslow-level details of the standard and enables encoding and decoding ofimage-derived information in DICOM format in a few lines of Python code. Thehighdicom library ties into the extensive Python ecosystem for image processingand machine learning. Simultaneously, by simplifying creation and parsing ofDICOM-compliant files, highdicom achieves interoperability with the medicalimaging systems that hold the data used to train and run ML models, andultimately communicate and store model outputs for clinical use. We demonstratethrough experiments with slide microscopy and computed tomography imaging,that, by bridging these two ecosystems, highdicom enables developers to trainand evaluate state-of-the-art ML models in pathology and radiology whileremaining compliant with the DICOM standard and interoperable with clinicalsystems at all stages. To promote standardization of ML research and streamlinethe ML model development and deployment process, we made the library availablefree and open-source.

Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology