# Sharing standardized image-derived data in computational pathology using DICOM

arXiv: https://arxiv.org/abs/2609.14530 · HTML: https://mlsanity.com/paper/2609.14530/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 74 references that are in the map.
- First posted on arXiv 13 Sep 2026.

## Builds on (most cited first, 7 of 7)

- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [Detection of prostate cancer in whole-slide images through end-to-end training with image-level labels](https://mlsanity.com/paper/2006.03394/) (2020)
- [Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images of 10 Cancer Types](https://mlsanity.com/paper/2002.07913/) (2020)
- [Tissue Concepts: supervised foundation models in computational pathology](https://mlsanity.com/paper/2409.03519/) (2024)
- [Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology](https://mlsanity.com/paper/2106.07806/) (2021)
- [The NCI Imaging Data Commons as a platform for reproducible research in computational pathology](https://mlsanity.com/paper/2303.09354/) (2023)
- [From Data to Diagnosis: A Large, Comprehensive Bone Marrow Dataset and AI Methods for Childhood Leukemia Prediction](https://mlsanity.com/paper/2509.15895/) (2025)

## Abstract (arXiv)

Development and evaluation of computational pathology methods require access to large and diverse datasets. Over the past decade, various initiatives invested significantly into collecting, centralizing, and sharing pathology imaging data. In contrast, sharing of image-derived data such as region-of-interest delineations or segmentation masks is less well developed. In this work, we describe our approach to encoding and sharing image-derived pathology data in a standardized manner within the National Cancer Institute (NCI) Imaging Data Commons (IDC), a platform that hosts and provides public access to de-identified radiology and pathology data. The IDC relies on the Digital Imaging and Communications in Medicine (DICOM) standard for data harmonization, yet the adoption of DICOM for pathology image-derived content has remained largely unexplored until now. Here, we present five representative datasets harmonized by conversion from their original representations into DICOM and shared publicly in the IDC. We demonstrate the benefits of this harmonization, describe contributions to critical open-source tooling, and discuss technical considerations relevant to broader adoption of DICOM for pathology image-derived data.