{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T00:49:17Z","timestamp":1774399757320,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T00:00:00Z","timestamp":1661126400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T00:00:00Z","timestamp":1661126400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["HHSN26110071"],"award-info":[{"award-number":["HHSN26110071"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["5R01CA235589"],"award-info":[{"award-number":["5R01CA235589"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["5P41EB015902"],"award-info":[{"award-number":["5P41EB015902"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000070","name":"National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"publisher","award":["P41EB028741"],"award-info":[{"award-number":["P41EB028741"]}],"id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["U24 CA264044"],"award-info":[{"award-number":["U24 CA264044"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Cancer Institute","award":["R01 CA241817"],"award-info":[{"award-number":["R01 CA241817"]}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["U01CA242879"],"award-info":[{"award-number":["U01CA242879"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Machine learning (ML) is revolutionizing image-based diagnostics in pathology and radiology. ML models have shown promising results in research settings, but the lack of interoperability between ML systems and enterprise medical imaging systems has been a major barrier for clinical integration and evaluation. The DICOM<jats:sup>\u00ae<\/jats:sup> standard specifies information object definitions (IODs) 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 datasets in DICOM format. Here we present the <jats:italic>highdicom<\/jats:italic> library, which provides a high-level application programming interface (API) 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 <jats:italic>highdicom<\/jats:italic> library leverages NumPy arrays for efficient data representation and ties into the extensive Python ecosystem for image processing and machine learning. Simultaneously, by simplifying creation and parsing of DICOM-compliant files, <jats:italic>highdicom<\/jats:italic> 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, <jats:italic>highdicom<\/jats:italic> enables developers and researchers 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 at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/herrmannlab\/highdicom\">https:\/\/github.com\/herrmannlab\/highdicom<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/s10278-022-00683-y","type":"journal-article","created":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T19:02:49Z","timestamp":1661194969000},"page":"1719-1737","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Highdicom: a Python Library for Standardized Encoding of Image Annotations and Machine Learning Model Outputs in Pathology and Radiology"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2242-351X","authenticated-orcid":false,"given":"Christopher P.","family":"Bridge","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chris","family":"Gorman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steven","family":"Pieper","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sean W.","family":"Doyle","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jochen K.","family":"Lennerz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jayashree","family":"Kalpathy-Cramer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David A.","family":"Clunie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andriy Y.","family":"Fedorov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Markus D.","family":"Herrmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,22]]},"reference":[{"issue":"7553","key":"683_CR1","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., and Hinton, G. \u201cDeep learning\u201d. Nature 521.7553 (2015), pp. 436\u2013444.","journal-title":"Nature"},{"issue":"8","key":"683_CR2","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1038\/s41591-019-0508-1","volume":"25","author":"G Campanella","year":"2019","unstructured":"Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K. J., Brogi, E., Reuter, V. E., Klimstra, D. S., and Fuchs, T. J. \u201cClinical-grade computational pathology using weakly supervised deep learning on whole slide images\u201d. Nature Medicine 25.8 (July 2019), pp. 1301\u20131309.","journal-title":"Nature Medicine"},{"issue":"7861","key":"683_CR3","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1038\/s41586-021-03512-4","volume":"594","author":"MY Lu","year":"2021","unstructured":"Lu, M. Y., Chen, T. Y., Williamson, D. F. K., Zhao, M., Shady, M., Lipkova, J., and Mahmood, F. \u201cAI-based pathology predicts origins for cancers of unknown primary\u201d. Nature 594.7861 (June 2021), pp. 106\u2013110.","journal-title":"Nature"},{"issue":"5","key":"683_CR4","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1038\/s41591-021-01343-4","volume":"27","author":"J van der Laak","year":"2021","unstructured":"Laak, J. van der, Litjens, G., and Ciompi, F. \u201cDeep learning in histopathology: the path to the clinic\u201d. Nat Med 27.5 (May 2021), pp. 775\u2013784.","journal-title":"Nat Med"},{"issue":"7788","key":"683_CR5","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1038\/s41586-019-1799-6","volume":"577","author":"SM McKinney","year":"2020","unstructured":"McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., Etemadi, M., Garcia-Vicente, F., Gilbert, F. J., Halling-Brown, M., Hassabis, D., Jansen, S., Karthikesalingam, A., Kelly, C. J., King, D., Ledsam, J. R., Melnick, D., Mostofi, H., Peng, L., Reicher, J. J., Romera-Paredes, B., Sidebottom, R., Suleyman, M., Tse, D., Young, K. C., De Fauw, J., and Shetty, S. \u201cInternational evaluation of an AI system for breast cancer screening\u201d. Nature 577.7788 (Jan. 2020), pp. 89\u201394.","journal-title":"Nature"},{"issue":"2","key":"683_CR6","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1148\/radiol.2018171820","volume":"288","author":"G Choy","year":"2018","unstructured":"Choy, G., Khalilzadeh, O., Michalski, M., Do, S., Samir, A. E., Pianykh, O. S., Geis, J. R., Pandharipande, P. V., Brink, J. A., and Dreyer, K. J. \u201cCurrent Applications and Future Impact of Machine Learning in Radiology\u201d. Radiology 288.2 (Aug. 2018), pp. 318\u2013328.","journal-title":"Radiology"},{"issue":"6","key":"683_CR7","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1038\/s41591-019-0447-x","volume":"25","author":"D Ardila","year":"2019","unstructured":"Ardila, D., Kiraly, A. P., Bharadwaj, S., Choi, B., Reicher, J. J., Peng, L., Tse, D., Etemadi, M., Ye, W., Corrado, G., Naidich, D. P., and Shetty, S. \u201cEnd-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography\u201d. Nat Med 25.6 (June 2019), pp. 954\u2013961.","journal-title":"Nat Med"},{"issue":"6468","key":"683_CR8","doi-asserted-by":"publisher","first-page":"955","DOI":"10.1126\/science.aay5189","volume":"366","author":"A Hosny","year":"2019","unstructured":"Hosny, A. and Aerts, H. J. W. L. \u201cArtificial intelligence for global health\u201d. Science 366.6468 (Nov. 2019), pp. 955\u2013956.","journal-title":"Science"},{"key":"683_CR9","doi-asserted-by":"crossref","unstructured":"Allen, B., Seltzer, S. E., Langlotz, C. P., Dreyer, K. P., Summers, R. M., Petrick, N., Marinac-Dabic, D., Cruz, M., Alkasab, T. K., Hanisch, R. J., Nilsen, W. J., Burleson, J., Lyman, K., and Kandarpa, K. \u201cA Road Map for Translational Research on Artificial Intelligence in Medical Imaging: From the 2018 National Institutes of Health\/RSNA\/ACR\/The Academy Workshop\u201d. J Am Coll Radiol 16.9 Pt A (Sept. 2019), pp. 1179\u20131189.","DOI":"10.1016\/j.jacr.2019.04.014"},{"issue":"5","key":"683_CR10","doi-asserted-by":"publisher","first-page":"619","DOI":"10.5858\/arpa.2016-0471-ED","volume":"141","author":"SR Granter","year":"2017","unstructured":"Granter, S. R., Beck, A. H., and Papke, D. J. \u201cAlphaGo, Deep Learning, and the Future of the Human Microscopist\u201d. Archives of Pathology & Laboratory Medicine 141.5 (2017), pp. 619\u2013621.","journal-title":"Archives of Pathology & Laboratory Medicine"},{"key":"683_CR11","doi-asserted-by":"crossref","unstructured":"Abels, E., Pantanowitz, L., Aeffner, F., Zarella, M. D., Laak, J. van der, Bui, M. M., Vemuri, V. N., Parwani, A. V., Gibbs, J., Agosto-Arroyo, E., Beck, A. H., and Kozlowski, C. \u201cComputational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association\u201d. J. Pathol. (July 2019).","DOI":"10.1002\/path.5331"},{"issue":"5","key":"683_CR12","first-page":"530","volume":"29","author":"CJ Roth","year":"2016","unstructured":"Roth, C. J., Lannum, L. M., and Persons, K. R. \u201cA Foundation for Enterprise Imaging: HIMSS-SIIM Collaborative White Paper\u201d. 29.5 (Oct. 2016), pp. 530\u2013538.","journal-title":"A Foundation for Enterprise Imaging: HIMSS-SIIM Collaborative White Paper"},{"key":"683_CR13","doi-asserted-by":"publisher","first-page":"6","DOI":"10.4103\/jpi.jpi_1_18","volume":"9","author":"D Clunie","year":"2018","unstructured":"Clunie, D., Hosseinzadeh, D., Wintell, M., De Mena, D., Lajara, N., Garcia-Rojo, M., Bueno, G., Saligrama, K., Stearrett, A., Toomey, D., Abels, E., Apeldoorn, F. V., Langevin, S., Nichols, S., Schmid, J., Horchner, U., Beckwith, B., Parwani, A., and Pantanowitz, L. \u201cDigital Imaging and Communications in Medicine Whole Slide Imaging Connectathon at Digital Pathology Association Pathology Visions 2017\u201d. Journal of Pathology Informatics 9 (2018), p. 6.","journal-title":"Journal of Pathology Informatics"},{"key":"683_CR14","doi-asserted-by":"publisher","first-page":"37","DOI":"10.4103\/jpi.jpi_42_18","volume":"9","author":"MD Herrmann","year":"2018","unstructured":"Herrmann, M. D., Clunie, D. A., Fedorov, A., Doyle, S. W., Pieper, S., Klepeis, V., Le, L. P., Mutter, G. L., Milstone, D. S., Schultz, T. J., Kikinis, R., Kotecha, G. K., Hwang, D. H., Andriole, K. P., Iafrate, A. J., Brink, J. A., Boland, G. W., Dreyer, K. J., Michalski, M., Golden, J. A., Louis, D. N., and Lennerz, J. K. \u201cImplementing the DICOM Standard for Digital Pathology\u201d. Journal of Pathology Informatics 9 (2018), p. 37.","journal-title":"Journal of Pathology Informatics"},{"key":"683_CR15","doi-asserted-by":"crossref","unstructured":"Dash, R., Jones, C., Merrick, R., Haroske, G., Harrison, J., Sayers, C., Haarselhorst, N., Wintell, M., Herrmann, M., and Macary, F. \u201cIntegrating the health-care enterprise pathology and laboratory medicine guideline for digital pathology interoperability\u201d. J Pathol Inform 12.16 (Mar. 2021).","DOI":"10.4103\/jpi.jpi_98_20"},{"key":"683_CR16","unstructured":"IHE PaLM Technical Committee in collaboration with DICOM WG26. IHE Pathology and Laboratory Medicine Technical Framework Supplement Digital Pathology Workflow \u2013 Image Acquisition (DPIA).\u00a0https:\/\/www.ihe.net\/uploadedFiles\/Documents\/PaLM\/IHE_PaLM_Suppl_DPIA.pdf. Aug. 2020."},{"key":"683_CR17","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.18","volume":"3","author":"MD Wilkinson","year":"2016","unstructured":"Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J. W., Silva Santos, L. B. da, Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., Gonzalez-Beltran, A., Gray, A. J., Groth, P., Goble, C., Grethe, J. S., Heringa, J., Hoen, P. A., Hooft, R., Kuhn, T., Kok, R., Kok, J., Lusher, S. J., Martone, M. E., Mons, A., Packer, A. L., Persson, B., Rocca-Serra, P., Roos, M., Schaik, R. van, Sansone, S. A., Schultes, E., Sengstag, T., Slater, T., Strawn, G., Swertz, M. A., Thompson, M., Lei, J. van der, Mulligen, E. van, Velterop, J., Waagmeester, A., Wittenburg, P., Wolstencroft, K., Zhao, J., and Mons, B. \u201cThe FAIR Guiding Principles for scientific data management and stewardship\u201d. Sci Data 3 (Mar. 2016), p. 160018.","journal-title":"Sci Data"},{"key":"683_CR18","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.2057","volume":"4","author":"A Fedorov","year":"2016","unstructured":"Fedorov, A., Clunie, D., Ulrich, E., Bauer, C., Wahle, A., Brown, B., Onken, M., Riesmeier, J., Pieper, S., Kikinis, R., Buatti, J., and Beichel, R. R. \u201cDICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET\/CT analysis results in head and neck cancer research\u201d. PeerJ 4 (2016), e2057.","journal-title":"PeerJ"},{"issue":"21","key":"683_CR19","doi-asserted-by":"publisher","first-page":"e87","DOI":"10.1158\/0008-5472.CAN-17-0336","volume":"77","author":"C Herz","year":"2017","unstructured":"Herz, C., Fillion-Robin, J. C., Onken, M., Riesmeier, J., Lasso, A., Pinter, C., Fichtinger, G., Pieper, S., Clunie, D., Kikinis, R., and Fedorov, A. \u201cdcmqi: An Open Source Library for Standardized Communication of Quantitative Image Analysis Results Using DICOM\u201d. Cancer Research 77.21 (2017), e87\u2013e90.","journal-title":"Cancer Research"},{"key":"683_CR20","unstructured":"IHE Radiology Technical Committee. IHE Radiology Technical Framework Supplement AI Results (AIR). https:\/\/www.ihe.net\/uploadedFiles\/Documents\/Radiology\/IHE_RAD_Suppl_AIR.pdf. June 2020."},{"key":"683_CR21","unstructured":"Virtanen, P. et al. \u201cSciPy 1.0: fundamental algorithms for scientific computing in Python\u201d. Nat Methods 17.3 (Mar. 2020), pp. 261\u2013272."},{"issue":"7825","key":"683_CR22","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","volume":"585","author":"CR Harris","year":"2020","unstructured":"Harris, C. R., Millman, K. J., Walt, S. J. van der, Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., Kerkwijk, M. H. van, Brett, M., Haldane, A., Del R\u00f3, J. F., Wiebe, M., Peterson, P., G\u00e9rard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E. \u201cArray programming with NumPy\u201d. Nature 585.7825 (Sept. 2020), pp. 357\u2013362.","journal-title":"Nature"},{"key":"683_CR23","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, \u00c9. \u201cScikit-Learn: Machine Learning in Python\u201d. J. Mach. Learn. Res. 12 (Nov. 2011), pp. 2825\u20132830.","journal-title":"J Mach Learn Res"},{"key":"683_CR24","doi-asserted-by":"crossref","unstructured":"Walt, S. van der, Sch\u00f6nberger, J., Nunez-Iglesias, J., Boulogne, F., Warner, J., Yager, N., Gouillart, E., Yu, T., and contributors, the scikit-image. \u201cscikit-image: image processing in Python\u201d. PeerJ 2 (2014), e453.","DOI":"10.7717\/peerj.453"},{"key":"683_CR25","doi-asserted-by":"crossref","unstructured":"Mason, D. \u201cSU-E-T-33: pydicom: an open source DICOM library\u201d. Medical Physics 38.6 Part 10 (2011), pp. 3493\u20133493.","DOI":"10.1118\/1.3611983"},{"key":"683_CR26","unstructured":"Hapke, H. and Nelson, C. Building machine learning pipelines. O\u2019Reilly Media, 2020."},{"key":"683_CR27","doi-asserted-by":"crossref","unstructured":"Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P., and Aroyo, L. M. \u201c\u201cEveryone Wants to Do the Model Work, Not the Data Work\u201d: Data Cascades in High-Stakes AI\u201d. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. CHI \u201921. Yokohama, Japan: Association for Computing Machinery, 2021.","DOI":"10.1145\/3411764.3445518"},{"key":"683_CR28","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang L. Bai, J., and Chintala, S. \u201cPyTorch: An Imperative Style, High-Performance Deep Learning Library\u201d. Advances in Neural Information Processing Systems 32. Curran Associates, Inc., 2019, pp. 8026\u20138037."},{"key":"683_CR29","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. \u201cTensorflow: A system for large-scale machine learning\u201d. 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). 2016, pp. 265\u2013283."},{"key":"683_CR30","unstructured":"Bradski, G. \u201cThe OpenCV Library\u201d. Dr. Dobb\u2019s Journal of Software Tools (2000)."},{"key":"683_CR31","unstructured":"Yoo, T. S., Ackerman, M. J., Lorensen, W. E., Schroeder, W., Chalana, V., Aylward, S., Metaxas, D., and Whitaker, R. \u201cEngineering and algorithm design for an image processing API: a technical report on ITK-the insight toolkit\u201d. Medicine Meets Virtual Reality 02\/10. IOS press, 2002, pp. 586\u2013592."},{"key":"683_CR32","volume-title":"Digital imaging and communications in medicine (DICOM): a practical introduction and survival guide,","author":"OS Pianykh","year":"2008","unstructured":"Pianykh, O. S. Digital imaging and communications in medicine (DICOM): a practical introduction and survival guide. Vol. 6. Springer, 2008."},{"issue":"3","key":"683_CR33","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/s13735-020-00195-x","volume":"9","author":"AM Hafiz","year":"2020","unstructured":"Hafiz, A. M. and Bhat, G. M. \u201cA survey on instance segmentation: state of the art\u201d. International Journal of Multimedia Information Retrieval 9.3 (2020), pp. 171\u2013189.","journal-title":"International Journal of Multimedia Information Retrieval"},{"issue":"4\u20135","key":"683_CR34","first-page":"404","volume":"37","author":"WD Bidgood","year":"1998","unstructured":"Bidgood, W. D. \u201cThe SNOMED DICOM microglossary: controlled terminology resource for data interchange in biomedical imaging\u201d. Methods Inf Med 37.4-5 (Nov. 1998), pp. 404\u2013414.","journal-title":"Methods Inf Med"},{"issue":"6","key":"683_CR35","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","volume":"26","author":"K Clark","year":"2013","unstructured":"Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., and Prior, F. \u201cThe Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository\u201d. Journal of Digital Imaging 26.6 (Dec. 2013), pp. 1045\u20131057.","journal-title":"Journal of Digital Imaging"},{"key":"683_CR36","doi-asserted-by":"publisher","first-page":"12","DOI":"10.4103\/jpi.jpi_93_18","volume":"10","author":"DA Clunie","year":"2019","unstructured":"Clunie, D. A. \u201cDual-Personality DICOM-TIFF for Whole Slide Images: A Migration Technique for Legacy Software\u201d. Journal of Pathology Informatics 10 (2019), p. 12.","journal-title":"Journal of Pathology Informatics"},{"issue":"2","key":"683_CR37","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1118\/1.3528204","volume":"38","author":"SG Armato III","year":"2011","unstructured":"Armato III, S. G., McLennan, G., Bidaut, L., McNitt-Gray, M. F., Meyer, C. R., Reeves, A. P., Zhao, B., Aberle, D. R., Henschke, C. I., Hoffman, E. A., Kazerooni, E. A., MacMahon, H., Beek, E. J. R. van, Yankelevitz, D., Biancardi, A. M., Bland, P. H., Brown, M. S., Engelmann, R. M., Laderach, G. E., Max, D., Pais, R. C., Qing, D. P.-Y., Roberts, R. Y., Smith, A. R., Starkey, A., Batra, P., Caligiuri, P., Farooqi, A., Gladish, G. W., Jude, C. M., Munden, R. F., Petkovska, I., Quint, L. E., Schwartz, L. H., Sundaram, B., Dodd, L. E., Fenimore, C., Gur, D., Petrick, N., Freymann, J., Kirby, J., Hughes, B., Vande Casteele, A., Gupte, S., Sallam, M., Heath, M. D., Kuhn, M. H., Dharaiya, E., Burns, R., Fryd, D. S., Salganicoff, M., Anand, V., Shreter, U., Vastagh, S., Croft, B. Y., and Clarke, L. P. \u201cThe Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans\u201d. Medical Physics 38.2 (2011), pp. 915\u2013931.","journal-title":"Medical Physics"},{"key":"683_CR38","volume-title":"Data from lidc-idri","author":"S Armato III","year":"2015","unstructured":"Armato III, S., McLennan, G., Bidaut, L., McNitt-Gray, M., Meyer, C., Reeves, A., Zhao, B., Aberle, D., Henschke, C., Hoffman, E., Kazerooni, E., MacMahon, H., Beek, E. van, Yankelevitz, D., Biancardi, A., Bland, P., Brown, M., Engelmann, R., Laderach, G., Max, D., Pais, R., Qing, D., Roberts, R., Smith, A., Starkey, A., Batra, P., Caligiuri, P., Farooqi, A., Gladish, G., Jude, C., Munden, R., Petkovska, I., Quint, L., Schwartz, L., Sundaram, B., Dodd, L., Fenimore, C., Gur, D., Petrick, N., Freymann, J., Kirby, J., Hughes, B., Casteele, A., Gupte, S., Sallam, M., Heath, M., Kuhn, M., Dharaiya, E., Burns, R., Fryd, D., Salganicoff, M., Anand, V., Shreter, U., Vastagh, S., Croft, B., and Clarke, L. Data From LIDC-IDRI. Tech. rep. The Cancer Imaging Archive, 2015."},{"issue":"10","key":"683_CR39","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1038\/s41591-018-0177-5","volume":"24","author":"N Coudray","year":"2018","unstructured":"Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyo, D., Moreira, A. L., Razavian, N., and Tsirigos, A. \u201cClassification and mutation prediction from non-small cell lung cancer histopathology images using deep learning\u201d. Nature Medicine 24.10 (2018), pp. 1559\u20131567.","journal-title":"Nature Medicine"},{"key":"683_CR40","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. \u201cDeep Residual Learning for Image Recognition\u201d. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016.","DOI":"10.1109\/CVPR.2016.90"},{"key":"683_CR41","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. \u201cImageNet: A Large-Scale Hierarchical Image Database\u201d. CVPR09. 2009.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"683_CR42","doi-asserted-by":"crossref","unstructured":"Lerousseau, M., Vakalopoulou, M., Classe, M., Adam, J., Battistella, E., Carr\u00e9, A., Estienne, T., Henry, T., Deutsch, E., and Paragios, N. \u201cWeakly Supervised Multiple Instance Learning Histopathological Tumor Segmentation\u201d. Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020. Springer International Publishing, 2020, pp. 470\u2013479.","DOI":"10.1007\/978-3-030-59722-1_45"},{"key":"683_CR43","doi-asserted-by":"crossref","unstructured":"Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Chen, R. J., Barbieri, M., and Mahmood, F. \u201cData-efficient and weakly supervised computational pathology on whole-slide images\u201d. Nat Biomed Eng (Mar. 2021).","DOI":"10.1038\/s41551-020-00682-w"},{"key":"683_CR44","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. \u201cFocal loss for dense object detection\u201d. Proceedings of the IEEE international conference on computer vision. 2017, pp. 2980\u20132988.","DOI":"10.1109\/ICCV.2017.324"},{"key":"683_CR45","unstructured":"DICOM Standards Committee. DICOM PS3.18 \u2013 Web Services. http:\/\/dicom.nema.org\/medical\/dicom\/current\/output\/chtml\/part18\/PS3.18.html. 2021."},{"issue":"3","key":"683_CR46","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1007\/s10278-019-00308-x","volume":"33","author":"R Gauriau","year":"2020","unstructured":"Gauriau, R., Bridge, C., Chen, L., Kitamura, F., Tenenholtz, N. A., Kirsch, J. E., Andriole, K. P., Michalski, M. H., and Bizzo, B. C. \u201cUsing DICOM Metadata for Radiological Image Series Categorization: a Feasibility Study on Large Clinical Brain MRI Datasets\u201d. J Digit Imaging 33.3 (June 2020), pp. 747\u2013762.","journal-title":"J Digit Imaging"},{"key":"683_CR47","doi-asserted-by":"crossref","unstructured":"Magrabi, F., Ammenwerth, E., McNair, J. B., De Keizer, N. F., Hypponen, H., Nyk\u00e4nen, P., Rigby, M., Scott, P. J., Vehko, T., Wong, Z. S.-Y., and Georgiou, A. \u201cArtificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications\u201d. Yearb Med Inform 28 (01 2019), pp. 128\u2013134.","DOI":"10.1055\/s-0039-1677903"},{"key":"683_CR48","unstructured":"DICOM Standards Committee, Working Group 26 (Pathology). Supplement 222: Microscopy Bulk Simple Annotations Storage SOP Class. ftp:\/\/medical.nema.org\/medical\/dicom\/supps\/LB\/sup222_lb_WSIAnnotations.pdf. 2021."},{"issue":"2","key":"683_CR49","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1007\/s10278-013-9657-9","volume":"27","author":"M Larobina","year":"2014","unstructured":"Larobina, M. and Murino, L. \u201cMedical image file formats\u201d. J Digit Imaging 27.2 (Apr. 2014), pp. 200\u2013206.","journal-title":"J Digit Imaging"},{"key":"683_CR50","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.jneumeth.2016.03.001","volume":"264","author":"X Li","year":"2016","unstructured":"Li, X., Morgan, P. S., Ashburner, J., Smith, J., and Rorden, C. \u201cThe first step for neuroimaging data analysis: DICOM to NIfTI conversion\u201d. J. Neurosci. Methods 264 (Apr. 2016), pp. 47\u201356.","journal-title":"J Neurosci Methods"},{"issue":"3","key":"683_CR51","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1038\/s42256-021-00307-0","volume":"3","author":"M Roberts","year":"2021","unstructured":"Roberts, M., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., Aviles-Rivero, A. I., Etmann, C., McCague, C., Beer, L., et al. \u201cCommon pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans\u201d. Nature Machine Intelligence 3.3 (2021), pp. 199\u2013217.","journal-title":"Nature Machine Intelligence"},{"key":"683_CR52","doi-asserted-by":"crossref","unstructured":"Fedorov, A., Longabaugh, W., Pot, W., Clunie, D., Pieper, S., Aerts Hugo, J., Homeyer, A., Lewis, R., Akbarzadeh, A., Bontempi, D., Clifford, D., Herrmann, M., H\u00f6fener, H., Octaviano, I., Osborne, C., Paquette, S., Petts, J., Punzo, D., Reyes, M., Schacherer, D., Tian, M., White, G., Ziegler, E., Shmulevich, I., Pihl, T., Wagner, U., Farahani, K., and R, K. \u201cNCI Imaging Data Commons\u201d. Cancer Research (2021).","DOI":"10.1158\/0008-5472.CAN-21-0950"}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-022-00683-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-022-00683-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-022-00683-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T19:22:43Z","timestamp":1669836163000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-022-00683-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,22]]},"references-count":52,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["683"],"URL":"https:\/\/doi.org\/10.1007\/s10278-022-00683-y","relation":{},"ISSN":["0897-1889","1618-727X"],"issn-type":[{"value":"0897-1889","type":"print"},{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,22]]},"assertion":[{"value":"28 October 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 May 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 May 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 August 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Christopher P. Bridge receives research funding from GE Healthcare, Nvidia Corporation, and Bayer, AG. Steven Pieper is in part supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) (P41 EB015902) and by the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l. Jayashree Kalpathy-Cramer is in part supported by the National Cancer Institute CI U01CA242879, and receives research funding from GE Healthcare, Genentech, Inc, and Bayer, AG. Andriy Y. Fedorov is in part supported by the National Institute of Biomedical Imaging and Bioengineering: contracts 75N92020C00008 and 75N92020C00021; grant P41 EB028741, and by the National Cancer Institute: Task Order No. HHSN26110071 under Contract No. HHSN261201500003l; grants U24 CA264044 and R01 CA241817. David A. Clunie receives financial compensation as a consultant of Philips Algotec, as a consultant for Essex Leidos CBIIT NCI, as a consultant for Brigham and Women\u2019s Hospital NCI Imaging Data Commons (IDC), as a consultant for the University of Leeds Northern Pathology Imaging Co-operative (NPIC), and as a contractor for NEMA as DICOM Editor. Markus D. Herrmann is in part supported by the National Cancer Institute: Task Order No. HHSN26110071 under Contract No. HHSN2612015000031. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}