Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Highdicom: A Python library for standardized encoding of image
  annotations and machine learning model outputs in pathology and radiology   Machine learning is revolutionizing image-based diagnostics in pathology and
radiology. ML models have shown promising results in research settings, but
their lack of interoperability has been a major barrier for clinical
integration and evaluation. The DICOM a standard specifies Information Object
Definitions and Services for the representation and communication of digital
images and related information, including image-derived annotations and
analysis results. However, the complexity of the standard represents an
obstacle for its adoption in the ML community and creates a need for software
libraries and tools that simplify working with data sets in DICOM format. Here
we present the highdicom library, which provides a high-level application
programming interface for the Python programming language that abstracts
low-level details of the standard and enables encoding and decoding of
image-derived information in DICOM format in a few lines of Python code. The
highdicom library ties into the extensive Python ecosystem for image processing
and machine learning. Simultaneously, by simplifying creation and parsing of
DICOM-compliant files, highdicom achieves interoperability with the medical
imaging systems that hold the data used to train and run ML models, and
ultimately communicate and store model outputs for clinical use. We demonstrate
through experiments with slide microscopy and computed tomography imaging,
that, by bridging these two ecosystems, highdicom enables developers to train
and evaluate state-of-the-art ML models in pathology and radiology while
remaining compliant with the DICOM standard and interoperable with clinical
systems at all stages. To promote standardization of ML research and streamline
the ML model development and deployment process, we made the library available
free and open-source.

31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph