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Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association
Open accessAbstract
In this white paper, experts from the Digital Pathology Association (DPA) define terminology and concepts in the emerging field of computational pathology, with a focus on its application to histology images analyzed together with their associated patient data to extract information. This review offers a historical perspective and describes the potential clinical benefits from research and applications in this field, as well as significant obstacles to adoption. Best practices for implementing computational pathology workflows are presented. These include infrastructure considerations, acquisition of training data, quality assessments, as well as regulatory, ethical, and cyber‐security concerns. Recommendations are provided for regulators, vendors, and computational pathology practitioners in order to facilitate progress in the field. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.
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LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
Deep learning in medical image analysis
Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968
Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., et al. (2017) · JAMA · doi:10.1001/jama.2017.14585 · Cited by 3,293
Self-citationOpportunities and obstacles for deep learning in biology and medicine
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310
The practical implementation of artificial intelligence technologies in medicine
He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019) · Nature Medicine · doi:10.1038/s41591-018-0307-0 · Cited by 2,246
Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks
Cireşan, D. C., Giusti, A., Gambardella, L. M., & Schmidhuber, J. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_51 · Cited by 1,536
Image analysis and machine learning in digital pathology: challenges and opportunities
Madabhushi, A., & Lee, G. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.06.037 · Cited by 1,093
Predicting cancer outcomes from histology and genomics using convolutional networks
Mobadersany, P., Yousefi, S., Amgad, M., Gutman, D. A., Barnholtz-Sloan, J. S., Velázquez Vega, J. E., Brat, D. J., & Cooper, L. A. D. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1717139115 · Cited by 1,084
Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., Samaras, D., Shroyer, K. R., Zhao, T., Batiste, R., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.03.086 · Cited by 1,027
Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification
Hou, L., Samaras, D., Kurc, T. M., Gao, Y., Davis, J. E., & Saltz, J. H. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.266 · Cited by 866
Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival
Beck, A. H., Sangoi, A. R., Leung, S., Marinelli, R. J., Nielsen, T. O., van de Vijver, M. J., West, R. B., van de Rijn, M., & Koller, D. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3002564 · Cited by 763
Self-citationA Survey of Methods for Explaining Black Box Models
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2019) · ACM Computing Surveys · doi:10.1145/3236009 · Cited by 629
Artificial Intelligence and Digital Pathology: Challenges and Opportunities
Tizhoosh, H. R., & Pantanowitz, L. (2018) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_53_18 · Cited by 544
Self-citationCruz-Roa, A., Gilmore, H., Basavanhally, A., Feldman, M., Ganesan, S., Shih, N. N. C., Tomaszewski, J., González, F. A., & Madabhushi, A. (2017) · Scientific Reports · doi:10.1038/srep46450 · Cited by 532
Steiner, D. F., MacDonald, R., Liu, Y., Truszkowski, P., Hipp, J. D., Gammage, C., Thng, F., Peng, L., & Stumpe, M. C. (2018) · American Journal of Surgical Pathology · doi:10.1097/pas.0000000000001151 · Cited by 489
Pesapane, F., Volonté, C., Codari, M., & Sardanelli, F. (2018) · Insights into Imaging · doi:10.1007/s13244-018-0645-y · Cited by 479
Automated Gleason grading of prostate cancer tissue microarrays via deep learning
Arvaniti, E., Fricker, K. S., Moret, M., Rupp, N., Hermanns, T., Fankhauser, C., Wey, N., Wild, P. J., Rüschoff, J. H., & Claassen, M. (2018) · Scientific Reports · doi:10.1038/s41598-018-30535-1 · Cited by 454
1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset
Litjens, G., Bandi, P., Ehteshami Bejnordi, B., Geessink, O., Balkenhol, M., Bult, P., Halilovic, A., Hermsen, M., van de Loo, R., Vogels, R., et al. (2018) · GigaScience · doi:10.1093/gigascience/giy065 · Cited by 418
Self-citationA Practical Guide to Whole Slide Imaging: A White Paper From the Digital Pathology Association
Zarella, M. D., Bowman;, D., Aeffner, F., Farahani, N., Xthona;, A., Absar, S. F., Parwani, A., Bui, M., & Hartman, D. J. (2019) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2018-0343-ra · Cited by 404
Self-citationAeffner, F., Zarella, M. D., Buchbinder, N., Bui, M. M., Goodman, M. R., Hartman, D. J., Lujan, G. M., Molani, M. A., Parwani, A. V., Lillard, K., et al. (2019) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_82_18 · Cited by 403
Self-citationQuestions for artificial intelligence in health care
Maddox, T. M., Rumsfeld, J. S., & Payne, P. R. O. (2019) · JAMA · doi:10.1001/jama.2018.18932 · Cited by 385
Structured crowdsourcing enables convolutional segmentation of histology images
Amgad, M., Elfandy, H., Hussein, H., Atteya, L. A., Elsebaie, M. A. T., Abo Elnasr, L. S., Sakr, R. A., Salem, H. S. E., Ismail, A. F., Saad, A. M., et al. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz083 · Cited by 315
Mitosis detection in breast cancer histological images An ICPR 2012 contest
Ludovic, R., Daniel, R., Nicolas, L., Maria, K., Humayun, I., Jacques, K., Frédérique, C., Catherine, G., Gilles, L. N., & Metin N, G. (2013) · Journal of Pathology Informatics · doi:10.4103/2153-3539.112693 · Cited by 302
Tellez, D., Balkenhol, M., Otte-Höller, I., van de Loo, R., Vogels, R., Bult, P., Wauters, C., Vreuls, W., Mol, S., Karssemeijer, N., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2820199 · Cited by 288
Pantanowitz, L., Sharma, A., Carter, A. B., Kurc, T., Sussman, A., & Saltz, J. (2018) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_69_18 · Cited by 282
Self-citationComputational pathology: Challenges and promises for tissue analysis
Fuchs, T. J., & Buhmann, J. M. (2011) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2011.02.006 · Cited by 265
The Gold Standard Paradox in Digital Image Analysis: Manual Versus Automated Scoring as Ground Truth
Aeffner, F., Wilson, K., Martin, N. T., Black, J. C., Hendriks, C. L. L., Bolon, B., Rudmann, D. G., Gianani, R., Koegler, S. R., Krueger, J., et al. (2017) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2016-0386-ra · Cited by 190
Self-citationEvans, A. J., Bauer, T. W., Bui, M. M., Cornish, T. C., Duncan, H., Glassy, E. F., Hipp, J., McGee, R. S., Murphy, D., Myers, C., et al. (2018) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2017-0496-cp · Cited by 168
Self-citationQaiser, T., Mukherjee, A., Chaitanya Reddy, P. B., Munugoti, S. D., Vamsi, T., Pitkäaho, T., Lehtimäki, T., Naughton, T. J., Berseth, M., Pedraza, A., et al. (2017) · Histopathology · doi:10.1111/his.13333 · Cited by 162
Weakly supervised mitosis detection in breast histopathology images using concentric loss
Li, C., Wang, X., Liu, W., Latecki, L. J., Wang, B., & Huang, J. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.013 · Cited by 160
Decision Support Systems in Oncology
Walsh, S., de Jong, E. E. C., van Timmeren, J. E., Ibrahim, A., Compter, I., Peerlings, J., Sanduleanu, S., Refaee, T., Keek, S., Larue, R. T. H. M., et al. (2019) · JCO Clinical Cancer Informatics · doi:10.1200/cci.18.00001 · Cited by 160
Implementing the DICOM Standard for Digital Pathology
Herrmann, M. D., Clunie, D., Fedorov, A., Doyle, S., Pieper, S. D., Klepeis, V. E., Le, L. P., Mutter, G. L., Milstone, D. S., Schultz, T. J., et al. (2018) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_42_18 · Cited by 155
Computational Pathology: A Path Ahead
Louis, D. N., Feldman, M. D., Carter, A. B., Dighe, A. S., Pfeifer, J. D., Bry, L., Almeida, J. S., Saltz, J., Braun, J., Tomaszewski, J. E., et al. (2015) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2015-0093-sa · Cited by 144
Irshad, H., Montaser‐Kouhsari, L., WALTZ, G., Bucur, O., Nowak, J. A., Dong, F., Knoblauch, N., & Beck, A. H. (2014) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789814644730_0029 · Cited by 141
Self-citationDong, F., Irshad, H., Oh, E.-Y., Lerwill, M. F., Brachtel, E. F., Jones, N. C., Knoblauch, N., Montaser‐Kouhsari, L., Johnson, N. B., Rao, L. K. F., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0114885 · Cited by 126
Self-citationThe General Data Protection Regulation (GDPR)
Murphy, J. (2018) · PubMed · Cited by 126
Color standardization and optimization in Whole Slide Imaging
Yagi, Y. (2011) · Diagnostic Pathology · doi:10.1186/1746-1596-6-s1-s15 · Cited by 123
Computational Pathology: An Emerging Definition
Louis, D. N., Gerber, G. K., Baron, J. M., Bry, L., Dighe, A. S., Getz, G. A., Higgins, J. M., Kuo, F. C., Lane, W. J., Michaelson, J. S., et al. (2014) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2014-0034-ed · Cited by 99
Kalpathy–Cramer, J., Freymann, J., Kirby, J., Kinahan, P. E., & Prior, F. (2014) · Translational Oncology · doi:10.1593/tlo.13862 · Cited by 92
Semantic annotation in biomedicine: the current landscape
Jovanović, J., & Ebrahim Bagheri, A. (2017) · Journal of Biomedical Semantics · doi:10.1186/s13326-017-0153-x · Cited by 73
Crowdsourcing the General Public for Large Scale Molecular Pathology Studies in Cancer
dos Reis, F. J. C., Lynn, S., Ali, H. R., Eccles, D., Hanby, A. M., Provenzano, E., Caldas, C., Howat, W., McDuffus, L., Liu, B., et al. (2015) · EBioMedicine · doi:10.1016/j.ebiom.2015.05.009 · Cited by 71
Origins of ... image analysis in clinical pathology.
Meijer, G. A., Beliën, J. A. M., van Diest, P. J., & Baak, J. P. A. (1997) · Journal of Clinical Pathology · doi:10.1136/jcp.50.5.365 · Cited by 70
Quanti.us: a tool for rapid, flexible, crowd-based annotation of images
Hughes, A. J., Mornin, J. D., Biswas, S. K., Beck, L. E., Bauer, D. P., Raj, A., Bianco, S., & Gartner, Z. J. (2018) · Nature Methods · doi:10.1038/s41592-018-0069-0 · Cited by 65
Tappeiner, E., Pröll, S., Hönig, M., Raudaschl, P., Zaffino, P., Spadea, M. F., Sharp, G. C., Schubert, R., & Fritscher, K. D. (2019) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-019-01922-4 · Cited by 55
Aeffner, F., Wilson, K., Bolon, B. N., Kanaly, S. T., Mahrt, C. R., Rudmann, D. G., Charles, E., & Young, G. D. (2016) · Toxicologic Pathology · doi:10.1177/0192623316653492 · Cited by 47
Self-citationWen, S., Kurç, T., Hou, L., Saltz, J. H., Gupta, R., Batiste, R. C., Zhao, T., Nguyen, V., Samaras, D., & Zhu, W. (2018) · PubMed Central · Cited by 46
An alternative reference space for H&E color normalization
Zarella, M. D., Yeoh, C., Breen, D. E., & Garcia, F. U. (2017) · PLoS ONE · doi:10.1371/journal.pone.0174489 · Cited by 40
Self-citationA Review of Medical Imaging Informatics
Sinha, U. S., Bui, A. A. T., Taira, R. K., Dionisio, J. D. N., Morioka, C. A., Johnson, D. B., & Kangarloo, H. (2002) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2002.tb04896.x · Cited by 38
Do We Need Annotation Experts? A Case Study in Celiac Disease Classification
Kwitt, R., Hegenbart, S., Rasiwasia, N., Vécsei, A., & Uhl, A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10470-6_57 · Cited by 26
A Review and Assessment Framework for Mobile-Based Emergency Intervention Apps
Yablowitz, M. G., & Schwartz, D. G. (2018) · ACM Computing Surveys · doi:10.1145/3145846 · Cited by 23
Estimation of Fine-Scale Histologic Features at Low Magnification
Zarella, M. D., Quaschnick, M. R., Breen, D. E., & Garcia, F. U. (2018) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2017-0380-oa · Cited by 12
Self-citationZarella, M. D., & Feldscher, A. (2018) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_47_18 · Cited by 9
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Metadata
- Authors
- Abels, E., Pantanowitz, L., Aeffner, F., Zarella, M. D., van der Laak, J., Bui, M. M., Vemuri, V. N., Parwani, A. V., Gibbs, J., Agosto‐Arroyo, E., Beck, A. H., & Kozlowski, C.
- Venue
- The Journal of Pathology
- Publisher
- Wiley
- Published
- 2019-09-03
- Volume
- 249
- Issue
- 3
- Pages
- 286-294
- ISSN
- 0022-3417 (ISSN-L)
- 1096-9896
- OpenAlex
- W2964756323
- Cited by
- 459
- References
- 55
- FWCI
- 19.92
- Type
- journal article
- Language
- en
- Open access
- hybrid
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