Translational AI and deep learning in diagnostic pathology
Open accessAbstract
There has been an exponential growth in the application of AI in health and in pathology. This is resulting in the innovation of deep learning technologies that are specifically aimed at cellular imaging and practical applications that could transform diagnostic pathology. This paper reviews the different approaches to deep learning in pathology, the public grand challenges that have driven this innovation and a range of emerging applications in pathology. The translation of AI into clinical practice will require applications to be embedded seamlessly within digital pathology workflows, driving an integrated approach to diagnostics and providing pathologists with new tools that accelerate workflow and improve diagnostic consistency and reduce errors. The clearance of digital pathology for primary diagnosis in the US by some manufacturers provides the platform on which to deliver practical AI. AI and computational pathology will continue to mature as researchers, clinicians, industry, regulatory organizations and patient advocacy groups work together to innovate and deliver new technologies to health care providers: technologies which are better, faster, cheaper, more precise, and safe.
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Recent advancement in AI-based deep learning technologies focusing on cellular images can transform education in diagnostic pathology.
“On the other hand, the recent advancement in AI-based deep learning technologies focusing on cellular images can transform education in diagnostic pathology [24].”
From Artificial Intelligence Revolutionizing the Field of Medical Education (2023) · cites this paper as [24]
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PD-1 and PD-L1 Immune Checkpoint Blockade to Treat Breast Cancer
Hartkopf, A. D., Taran, F., Wallwiener, M., Walter, C. B., Krämer, B., Grischke, E., & Brucker, S. Y. (2016) · Breast Care · doi:10.1159/000453569 · Cited by 30,989 · see citing works
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602 · see citing works
Nishimura, R., Osako, T., Nishiyama, Y., Tashima, R., Nakano, M., Fujisue, M., Toyozumi, Y., & Arima, N. (2013) · Oncology · doi:10.1159/000353099 · Cited by 13,239 · see citing works
Comprehensive molecular portraits of human breast tumours
The Cancer Genome Atlas Network (2012) · Nature · doi:10.1038/nature11412 · Cited by 12,129 · see citing works
Generative Adversarial Networks
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.2661 · Cited by 4,477 · see citing works
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196 · see citing works
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 · see citing works
Epstein, J. I., Egevad, L., Amin, M. B., Delahunt, B., Srigley, J. R., & Humphrey, P. A. (2015) · The American Journal of Surgical Pathology · doi:10.1097/pas.0000000000000530 · Cited by 3,246 · see citing works
Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018) · Nature Medicine · doi:10.1038/s41591-018-0177-5 · Cited by 2,930 · see citing works
Epstein, J. I., Allsbrook, W. C., Amin, M. B., & Egevad, L. L. (2005) · The American Journal of Surgical Pathology · doi:10.1097/01.pas.0000173646.99337.b1 · Cited by 2,763 · see citing works
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
Schlegl, T., Seeböck, P., Waldstein, S. M., Schmidt-Erfurth, U., & Langs, G. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-59050-9_12 · Cited by 2,627 · see citing works
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial NetworksPreprint
Zhu, J.-Y., Park, T., Isola, P., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.10593 · Cited by 2,278 · see citing works
U-Net: deep learning for cell counting, detection, and morphometry
Falk, T., Mai, D., Bensch, R., Çiçek, Ö., Abdulkadir, A., Marrakchi, Y., Böhm, A., Deubner, J., Jäckel, Z., Seiwald, K., et al. (2018) · Nature Methods · doi:10.1038/s41592-018-0261-2 · Cited by 1,912 · see citing works
The Inevitable Application of Big Data to Health Care
Murdoch, T. B., & Detsky, A. S. (2013) · JAMA · doi:10.1001/jama.2013.393 · Cited by 1,676 · see citing works
f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks
Schlegl, T., Seeböck, P., Waldstein, S. M., Langs, G., & Schmidt-Erfurth, U. M. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.010 · Cited by 1,561 · see citing works
Exome sequencing identifies recurrent SPOP, FOXA1 and MED12 mutations in prostate cancer
Barbieri, C. E., Baca, S. C., Lawrence, M. S., Demichelis, F., Blattner, M., Theurillat, J.-P. P., White, T. A., Stojanov, P., Allen, E. M. V., Stransky, N., et al. (2012) · Nature Genetics · doi:10.1038/ng.2279 · Cited by 1,550 · see citing works
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 · see citing works
Deep Learning Applications in Medical Image Analysis
Ker, J., Wang, L., Rao, J. P., & Lim, C. C. T. (2017) · IEEE Access · doi:10.1109/access.2017.2788044 · Cited by 1,521 · see citing works
Going Deeper with Convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2014) · arXiv · doi:10.48550/arxiv.1409.4842 · Cited by 1,461 · see citing works
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
(2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67558-9 · Cited by 1,149 · see citing works
A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology
Kumar, N., Verma, R., Sharma, S., Bhargava, S., Vahadane, A., & Sethi, A. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2677499 · Cited by 1,136 · see citing works
AutoAugment: Learning Augmentation Policies from Data
Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., & Le, Q. V. (2018) · arXiv · doi:10.48550/arxiv.1805.09501 · Cited by 1,106 · see citing works
U-Net: Convolutional Networks for Biomedical Image SegmentationPreprint
Ronneberger, O., Fischer, P., & Brox, T. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.04597 · Cited by 1,079 · see citing works
Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis
Litjens, G., Sánchez, C. I., Timofeeva, N., Hermsen, M., Nagtegaal, I., Kovacs, I., Hulsbergen - van de Kaa, C., Bult, P., van Ginneken, B., & van der Laak, J. (2016) · Scientific Reports · doi:10.1038/srep26286 · Cited by 1,069 · see citing works
Nuclei Segmentation with Recurrent Residual Convolutional Neural Networks based U-Net (R2U-Net)
Alom, M. Z., Yakopcic, C., Taha, T. M., & Asari, V. K. (2018) · IEEE National Aerospace and Electronics Conference (NAECON) · doi:10.1109/naecon.2018.8556686 · Cited by 1,069 · see citing works
Deep Learning for Identifying Metastatic Breast CancerPreprint
Wang, D., Khosla, A., Gargeya, R., Irshad, H., & Beck, A. H. (2016) · arXiv.org · doi:10.48550/arxiv.1606.05718 · Cited by 1,028 · see citing works
Classification of breast cancer histology images using Convolutional Neural Networks
Araújo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., Polónia, A., & Campilho, A. (2017) · PLoS ONE · doi:10.1371/journal.pone.0177544 · Cited by 1,017 · see citing works
Gland segmentation in colon histology images: The glas challenge contest
Sirinukunwattana, K., Pluim, J. P. W., Chen, H., Qi, X., Heng, P., Guo, Y. B., Wang, L. Y., Matuszewski, B. J., Bruni, E., Sanchez, U., et al. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.08.008 · Cited by 989 · see citing works
BACH: Grand challenge on breast cancer histology images
Aresta, G., Araújo, T., Kwok, S., Chennamsetty, S. S., Safwan, M., Alex, V., Marami, B., Prastawa, M. W., Chan, M. S. M., Donovan, M. J., et al. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.05.010 · Cited by 717 · see citing works
An International Ki67 Reproducibility Study
Polley, M. C., Leung, S. C. Y., McShane, L. M., Gao, D., Hugh, J. C., Mastropasqua, M. G., Viale, G., Zabaglo, L. A., Penault-Llorca, F. M., Bartlett, J. M. S., et al. (2013) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djt306 · Cited by 593 · see citing works
Bandi, P., Geessink, O., Manson, Q., Van Dijk, M., Balkenhol, M., Hermsen, M., Ehteshami Bejnordi, B., Lee, B., Paeng, K., Zhong, A., et al. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2867350 · Cited by 569 · see citing works
Detecting Cancer Metastases on Gigapixel Pathology Images
Liu, Y., Gadepalli, K., Norouzi, M., Dahl, G. E., Kohlberger, T., Boyko, A., Venugopalan, S., Timofeev, A., Nelson, P. Q., Corrado, G. S., et al. (2017) · arXiv · doi:10.48550/arxiv.1703.02442 · Cited by 506 · see citing works
DCAN: Deep contour-aware networks for object instance segmentation from histology images
Chen, H., Qi, X., Yu, L., Dou, Q., Qin, J., & Heng, P. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.11.004 · Cited by 502 · see citing works
Nagpal, K., Foote, D., Liu, Y., Chen, P.-H. C., Wulczyn, E., Tan, F., Olson, N., Smith, J. L., Mohtashamian, A., Wren, J. H., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0112-2 · Cited by 495 · see citing works
Artificial Intelligence in Drug Discovery and Development
Agrawal, P. (2018) · Journal of Pharmacovigilance · doi:10.4172/2329-6887.1000e173 · Cited by 493 · see citing works
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 · see citing works
Assessment of algorithms for mitosis detection in breast cancer histopathology images
Veta, M., van Diest, P. J., Willems, S. M., Wang, H., Madabhushi, A., Cruz-Roa, Á., Gonzalez, F. A., Larsen, A., Vestergaard, J. S., Dahl, A. B., et al. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.11.010 · Cited by 480 · see citing works
Automated Gleason grading of prostate cancer tissue microarrays via deep learning
Arvaniti, E., Fricker, K. S., Moret, M., Rupp, N. J., Hermanns, T., Fankhauser, C. D., Wey, N., Wild, P. J., Rüschoff, J. H., & Claassen, M. (2018) · Scientific Reports · doi:10.1038/s41598-018-30535-1 · Cited by 447 · see citing works
Artificial intelligence in medicine: current trends and future possibilities
Buch, V., Ahmed, I., & Maruthappu, M. (2018) · British Journal of General Practice · doi:10.3399/bjgp18x695213 · Cited by 446 · see citing works
1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset
Litjens, G. J. S., Bándi, P., Bejnordi, B. E., Geessink, O. G. F., Balkenhol, M. C. A., Bult, P., Halilović, A., Hermsen, M., van de Loo, R., Vogels, R. J., et al. (2018) · GigaScience · doi:10.1093/gigascience/giy065 · Cited by 414 · see citing works
Mukhopadhyay, S., Feldman, M. D., Abels, E., Ashfaq, R., Beltaifa, S., Cacciabeve, N. G., Cathro, H. P., Cheng, L., Cooper, K., Dickey, G. E., et al. (2018) · American Journal of Surgical Pathology · doi:10.1097/pas.0000000000000948 · Cited by 399 · see citing works
Staingan: Stain Style Transfer for Digital Histological Images
Shaban, M., Baur, C., Navab, N., & Albarqouni, S. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759152 · Cited by 361 · see citing works
Sharma, H., Zerbe, N., Klempert, I., Hellwich, O., & Hufnagl, P. (2017) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2017.06.001 · Cited by 316 · see citing works
Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge
Veta, M., Heng, Y. J., Stathonikos, N., Bejnordi, B. E., de Beça, F. F., Wollmann, T., Rohr, K., Shah, M., Wang, D., Rousson, M., et al. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.02.012 · Cited by 309 · see citing works
Mitosis detection in breast cancer histological images An ICPR 2012 contest
Roux, L., Racoceanu, D., Loménie, N., Kulikova, M. S., Irshad, H., Jacques, K., Capron, F., Genestie, C., Naour, G. L., & Gurcan Metin, N. (2013) · Journal of Pathology Informatics · doi:10.4103/2153-3539.112693 · Cited by 303 · see citing works
Deep Learning Algorithms for Detection of Lymph Node Metastases From Breast Cancer
Golden, J. A. (2017) · JAMA · doi:10.1001/jama.2017.14580 · Cited by 297 · see citing works
An application of machine learning to haematological diagnosis
Gunčar, G., Kukar, M., Notar, M., Brvar, M., Černelč, P., Notar, M., & Notar, M. (2018) · Scientific Reports · doi:10.1038/s41598-017-18564-8 · Cited by 237 · see citing works
An international study to increase concordance in Ki67 scoring
Polley, M. C., Leung, S. C. Y., Gao, D., Mastropasqua, M. G., Zabaglo, L. A., Bartlett, J. M. S., McShane, L. M., Enos, R. A., Badve, S., Bane, A. L., et al. (2015) · Modern Pathology · doi:10.1038/modpathol.2015.38 · Cited by 214 · see citing works
The estimation of tumor cell percentage for molecular testing by pathologists is not accurate
Smits, A. J. J., Kummer, J. A., de Bruin, P. C., Bol, M., van den Tweel, J. G., Seldenrijk, K. A., Willems, S. M., Offerhaus, G. J. A., de Weger, R. A., van Diest, P. J., et al. (2013) · Modern Pathology · doi:10.1038/modpathol.2013.134 · Cited by 213 · see citing works
Learning to Compose Domain-Specific Transformations for Data AugmentationPreprint
Ratner, A., Ehrenberg, H. R., Hussain, Z., Dunnmon, J., & Ré, C. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.01643 · Cited by 212 · see citing works
Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer
Vandenberghe, M. E., Scott, M. L. J., Scorer, P., Söderberg, M. P., Balcerzak, D., & Barker, C. (2017) · Scientific Reports · doi:10.1038/srep45938 · Cited by 210 · see citing works
Udall, M., Rizzo, M., Kenny, J., Doherty, J. P., Dahm, S., Robbins, P. B., & Faulkner, E. C. (2018) · Diagnostic Pathology · doi:10.1186/s13000-018-0689-9 · Cited by 209 · see citing works
Xie, Y., Xing, F., Kong, X., Su, H., & Yang, L. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_43 · Cited by 204 · see citing works
Qaiser, 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 · see citing works
Leung, S. C. Y., Nielsen, T. O., Zabaglo, L. A., Arun, I., Badve, S., Bane, A. L., Bartlett, J. M. S., Borgquist, S., Chang, M. C., Dodson, A. R., et al. (2016) · npj Breast Cancer · doi:10.1038/npjbcancer.2016.14 · Cited by 153 · see citing works
Automated Image Analysis for High-Content Screening and Analysis
Shariff, A., Kangas, J. D., Coelho, L. P., Quinn, S. P., & Murphy, R. F. (2010) · SLAS DISCOVERY · doi:10.1177/1087057110370894 · Cited by 140 · see citing works
for The Trans-Atlantic Prostate Group, Berney, D. M., Gopalan, A., Kudahetti, S. C., Fisher, G., Ambroisine, L., Foster, C. S., Reuter, V., Eastham, J. A., Møller, H., et al. (2009) · British Journal of Cancer · doi:10.1038/sj.bjc.6604951 · Cited by 135 · see citing works
Evaluating the benefits of digital pathology implementation: time savings in laboratory logistics
Baidoshvili, A., Bucur, A., van Leeuwen, J., van der Laak, J. A. W. M., Kluin, P. M., & van Diest, P. J. (2018) · Histopathology · doi:10.1111/his.13691 · Cited by 129 · see citing works
Deep Learning Based Automatic Immune Cell Detection for Immunohistochemistry Images
Chen, T., & Chefd’hotel, C. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10581-9_3 · Cited by 117 · see citing works
The molecular basis of breast cancer pathological phenotypes
Heng, Y. J., Lester, S. C., Tse, G. M.-K., Factor, R. E., Allison, K. H., Collins, L. C., Chen, Y., Jensen, K. C., Johnson, N. B., Jeong, J. C., et al. (2016) · The Journal of Pathology · doi:10.1002/path.4847 · Cited by 110 · see citing works
Kapil, A., Meier, A., Zuraw, A., Steele, K. E., Rebelatto, M. C., Schmidt, G., & Brieu, N. (2018) · Scientific Reports · doi:10.1038/s41598-018-35501-5 · Cited by 110 · see citing works
Viray, H., Li, K., Long, T. A., Vasalos, P., Bridge, J. A., Jennings, L. J., Halling, K. C., Hameed, M., & Rimm, D. L. (2013) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2012-0561-cp · Cited by 107 · see citing works
McKenney, J. K., Simko, J. P., Bonham, M. J., True, L. D., Troyer, D. A., Hawley, S. J., Newcomb, L. F., Fazli, L., Kunju, L. P., Nicolas, M. M., et al. (2011) · The Journal of Urology · doi:10.1016/j.juro.2011.03.115 · Cited by 107 · see citing works
Klauschen, F., Wienert, S., Schmitt, W., Loibl, S., Gerber, B., Blohmer, J., Huober, J., Rüdiger, T., Erbstößer, E., Mehta, K. M., et al. (2014) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-14-1283 · Cited by 103 · see citing works
Prognostic role of Ki-67 score in localized prostate cancer: A systematic review and meta-analysis
Berlín, A., Castro-Mesta, J. F., Rodríguez‐Romo, L., Barajas, D. H., González-Guerrero, J. F., Rodríguez-Fernández, I. A., Conchas, G. A. G., Verdines-Perez, A. M., & Vera-Badillo, F. (2017) · Urologic Oncology Seminars and Original Investigations · doi:10.1016/j.urolonc.2017.05.004 · Cited by 98 · see citing works
Automatic Ki-67 Counting Using Robust Cell Detection and Online Dictionary Learning
Xing, F., Su, H., Neltner, J. H., & Yang, L. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2291703 · Cited by 96 · see citing works
BESNet: Boundary-Enhanced Segmentation of Cells in Histopathological Images
Oda, H., Roth, H. R., Chiba, K., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00934-2_26 · Cited by 90 · see citing works
Endl, E., & Gerdes, J. (2000) · Journal of Cellular Physiology · doi:10.1002/(sici)1097-4652(200003)182:3 · Cited by 89 · see citing works
A Fast and Refined Cancer Regions Segmentation Framework in Whole-slide Breast Pathological Images
Guo, Z., Liu, H., Ni, H., Wang, X., Su, M., Guo, W., Wang, K., Jiang, T., & Qian, Y. (2019) · Scientific Reports · doi:10.1038/s41598-018-37492-9 · Cited by 88 · see citing works
Cell Counting by Regression Using Convolutional Neural Network
Xue, Y., Ray, N., Hugh, J. C., & Bigras, G. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46604-0_20 · Cited by 86 · see citing works
H&E-stained Whole Slide Image Deep Learning Predicts SPOP Mutation State in Prostate CancerPreprint
Schaumberg, A. J., Rubin, M. A., & Fuchs, T. J. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/064279 · Cited by 82 · see citing works
Deep Voting: A Robust Approach Toward Nucleus Localization in Microscopy Images
Xie, Y., Kong, X., Xing, F., Liu, F., Su, H., & Yang, L. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_45 · Cited by 79 · see citing works
Learning to Compose Domain-Specific Transformations for Data Augmentation.
Ratner, A., Ehrenberg, H. R., Hussain, Z., Dunnmon, J., & Ré, C. (2017) · PubMed · Cited by 79 · see citing works
Abubakar, M., Orr, N., Daley, F. M., Coulson, P., Ali, H. R., Blows, F. M., Benitez, J. J., Milne, R. L., Brenner, H., Stegmaier, C., et al. (2016) · Breast Cancer Research · doi:10.1186/s13058-016-0765-6 · Cited by 78 · see citing works
Lejeune, M., Jaén, J., Pons, L. E., López, C., Salvadó-Usach, T., Bosch, R., García, M., Escrivá, P., Baucells, J., Cugat, X., et al. (2008) · Journal of Anatomy · doi:10.1111/j.1469-7580.2008.00910.x · Cited by 76 · see citing works
A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology
Paeng, K., Hwang, S., Park, S., & Kim, M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67558-9_27 · Cited by 73 · see citing works
del Toro, Ó. J., Atzori, M., Otálora, S., Andersson, M., Eurén, K., Hedlund, M., Rönnquist, P., & Müller, H. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2255710 · Cited by 73 · see citing works
Multiclass Classification of Breast Cancer in Whole-Slide Images
Kwok, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93000-8_106 · Cited by 72 · see citing works
Robboy, S. J., Gupta, S., Crawford, J. M., Cohen, M. B., Karcher, D. S., Leonard, D. G. B., Magnani, B., Novis, D. A., Prystowsky, M. B., Powell, S. Z.-E., et al. (2015) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2014-0559-oa · Cited by 62 · see citing works
Humphries, M. P., Hynes, S. O., Bingham, V., Cougot, D., James, J. A., Patell-Socha, F., Parkes, E. E., Blayney, J. K., O’Rorke, M. A., Irwin, G. W., et al. (2018) · Journal of Oncology · doi:10.1155/2018/2937012 · Cited by 62 · see citing works
Automated tumor analysis for molecular profiling in lung cancer
Hamilton, P. W., Wang, Y., Boyd, C., James, J. A., Loughrey, M. B., Hougton, J. P., Boyle, D. P., Kelly, P., Maxwell, P., McCleary, D. F., et al. (2015) · Oncotarget · doi:10.18632/oncotarget.4391 · Cited by 58 · see citing works
From Machine Learning to Artificial Intelligence Applications in Cardiac Care
Tsay, D., & Patterson, C. (2018) · Circulation · doi:10.1161/circulationaha.118.031734 · Cited by 57 · see citing works
Humphries, M. P., McQuaid, S., Craig, S. G., Bingham, V., Maxwell, P., Maurya, M., McLean, F., Sampson, J., Higgins, P., Greene, C., et al. (2018) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2018.09.025 · Cited by 50 · see citing works
Vestjens, J. H. M. J., Pepels, M. J. A. E., de Boer, M., Borm, G. F., van Deurzen, C. H. M., van Diest, P. J., van Dijck, J. A. A. M., Adang, E., Nortier, J. W. R., Rutgers, E. J. T., et al. (2012) · Annals of Oncology · doi:10.1093/annonc/mds072 · Cited by 47 · see citing works
Kumar, A., Rao, A., Bhavani, S., Newberg, J. Y., & Murphy, R. F. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1415120112 · Cited by 46 · see citing works
A Deep Learning Approach for Rapid Mutational Screening in MelanomaPreprint
Kim, R. H., Nomikou, S., Coudray, N., Jour, G., Dawood, Z., Hong, R., Esteva, E., Sakellaropoulos, T., Donnelly, D. M., Moran, U., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/610311 · Cited by 46 · see citing works
Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-Slide Images
Dong, N., Kampffmeyer, M., Liang, X., Wang, Z., Dai, W., & Xing, E. P. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00889-5_36 · Cited by 45 · see citing works
Singh, R., Agashe, S., Gosavi, A., & Sulhyan, K. (2011) · Indian Journal of Cancer · doi:10.4103/0019-509x.92277 · Cited by 33 · see citing works
Patch-level Tumor Classification in Digital Histopathology Images with Domain Adapted Deep Learning
Xia, T., Kumar, A., Feng, D. D., & Kim, J. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8512353 · Cited by 29 · see citing works
Cell Detection in Microscopy Images with Deep Convolutional Neural Network and Compressed SensingPreprint
Xue, Y., & Ray, N. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.03307 · Cited by 27 · see citing works
Zehntner, S. P., Chakravarty, M. M., Bolovan, R. J., Chan, C., & Bedell, B. J. (2008) · Journal of Histochemistry & Cytochemistry · doi:10.1369/jhc.2008.950345 · Cited by 26 · see citing works
Virtualization of Tissue Staining in Digital Pathology Using an Unsupervised Deep Learning Approach
Lahiani, A., Gildenblat, J., Klaman, I., Albarqouni, S., Navab, N., & Klaiman, E. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-23937-4_6 · Cited by 26 · see citing works
Zhou, N., Fedorov, A., Fennessy, F. M., Kikinis, R., & Gao, Y. D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.02678 · Cited by 23 · see citing works
Glory, E., Newberg, J. Y., & Murphy, R. F. (2008) · IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI) · doi:10.1109/isbi.2008.4540993 · Cited by 22 · see citing works
Pattern Recognition in Histopathological Images: An ICPR 2010 Contest
Gürcan, M. N., Madabhushi, A., & Rajpoot, N. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-17711-8_23 · Cited by 21 · see citing works
Automated Objective Determination of Percentage of Malignant Nuclei for Mutation Testing
Viray, H., Coulter, M., Li, K., Lane, K., Madan, A., Mitchell, K. A., Schalper, K. A., Hoyt, C. C., & Rimm, D. L. (2013) · Applied immunohistochemistry & molecular morphology · doi:10.1097/pai.0b013e318299a1f6 · Cited by 19 · see citing works
Is Ki67 Prognostic for Aggressive Prostate Cancer? A Multicenter Real-World Study
Fantony, J. J., Howard, L. E., Csizmadi, I., Armstrong, A. J., Lark, A. L., Galet, C., Aronson, W. J., & Freedland, S. J. (2018) · Biomarkers in Medicine · doi:10.2217/bmm-2017-0322 · Cited by 19 · see citing works
Wang, C., Lee, Y., Calista, E., Zhou, F., Hongtu Zhu, B., Suzuki, R. H., Komura, D., Ishikawa, S., & Cheng, S. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx838 · Cited by 18 · see citing works
Automating proliferation rate estimation from Ki-67 histology images
Al-Lahham, H. Z., Alomari, R. S., Hiary, H., & Chaudhary, V. (2012) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.911009 · Cited by 18 · see citing works
Jonnalagedda, P., Schmolze, D. B., & Bhanu, B. (2018) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2018.00044 · Cited by 15 · see citing works
Lahiani, A., Gildenblat, J., Klaman, I., Navab, N., & Klaiman, E. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.06958 · Cited by 10 · see citing works
Digital and Computational Pathology for Biomarker Discovery
Hamilton, P., O’Reilly, P. G., Bankhead, P., Abels, E., & Salto‐Tellez, M. (2018) · Springer eBooks · doi:10.1007/978-3-319-95228-4_7 · Cited by 10 · see citing works
Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide ImagesPreprint
Dong, N., Kampffmeyer, M., Liang, X., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.11113 · Cited by 9 · see citing works
DeepSDCS: Dissecting cancer proliferation heterogeneity in Ki67 digital whole slide imagesPreprint
Narayanan, P., Raza, S. E. A., Dodson, A. R., Gusterson, B. A., Dowsett, M., & Yuan, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.10850 · Cited by 8 · see citing works
A deep multiple instance model to predict prostate cancer metastasis from nuclear morphology
Ing, N., Tomczak, J. M., Miller, E., Garraway, I. P., Welling, M., Knudsen, B. S., & Gertych, A. (2018) · Cited by 7 · see citing works
A Unified Framework for Tumor Proliferation Score Prediction in Breast HistopathologyPreprint
Paeng, K., Hwang, S., Park, S., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.07180 · Cited by 5 · see citing works
Han, J. G., Shin, D., Arthur, G. L., & Shyu, C. (2010) · IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) · doi:10.1109/bibmw.2010.5703949 · Cited by 4 · see citing works
Veta, M. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.05.008 · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 6
- [24]Xiao K Wang Z Xu T Wan T A Deep Learning Method for Detecting and Classifying Breast Cancer Metastases in Lymph Nodes on Histopathological Images. (2017). Available online at: https://www.semanticscholar.org/paper/A-DEEP-LEARNING-METHOD-FOR-DETECTING-AND-BREAST-IN-Xiao-Wang/72ed2f4b2b464e36f85c70dcf660f4bb9468c64c (accessed March 31, 2019).
- [41]Roux L Detection of mitosis and evaluation of nuclear atypia score in breast cancer histological images. In: 22nd International Conference on Pattern Recognition 2014. MITOS-ATYPIA Contest. (2014). Available online at: http://ludo17.free.fr/mitos_atypia_2014/icpr2014_MitosAtypia_DataDescription.pdf
- [42]Chen H Dou Q Wang X Qin J Heng P-A Mitosis detection in breast cancer histology images via deep cascaded networks. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. AAAI Press (2016). p. 1160– 6. Available online at: http://dl.acm.org/citation.cfm?id=3015812.3015984
- [66]Bulten W Litjens GJS Hulsbergen-van de Kaa CA van der Laak J Automated segmentation of epithelial tissue in prostatectomy slides using deep learning. In: Proceedings Medical Imaging 2018: Digital Pathology. (2018). p. 27. Available online at: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10581/2292872/Automated-segmentation-of-epithelial-tissue-in-prostatectomy-slides-using-deep/10.1117/12.2292872.full (accessed April 1, 2019). · DOI
- [68]Kallen H Molin J Heyden A Lundstrom C Astrom K Towards grading gleason score using generically trained deep convolutional neural networks. In: 2016 IEEE 13th Int Symp on Biomed Im (ISBI). (2016). p. 1163– 7. Available online at: http://ieeexplore.ieee.org/document/7493473/ (accessed April 1, 2019).
- [105]Garcia E Hermoza R Castanon CB Cano L Castillo M Castanneda C Automatic lymphocyte detection on gastric cancer ihc images using deep learning. In: IEEE 30th International Symposium on Computer Based Medical Systems (CBMS). (2017). p. 200– 4. Available inline at: http://ieeexplore.ieee.org/document/8104187/ (accessed April 1, 2019).
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Park, J. H., Kim, E. Y., Luchini, C., Eccher, A., Tizaoui, K., Shin, J. I., & Lim, B. J. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23052462 · Cited by 38 · see citing works
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Rashidi, H. H., Bowers, K., & Gil, M. R. (2023) · Journal of Thrombosis and Haemostasis · doi:10.1016/j.jtha.2022.12.019 · Cited by 33 · see citing works
Recent Advances in Melanoma Diagnosis and Prognosis Using Machine Learning Methods
Grossarth, S. N., Mosley, D. A., Madden, C., Ike, J., Smith, I. T., Huo, Y., & Wheless, L. (2023) · Current Oncology Reports · doi:10.1007/s11912-023-01407-3 · Cited by 33 · see citing works
Arun, S., Grosheva, M. N., Kosenko, M. G., Robertus, J. L., Blyuss, O. B., Gabe, R., Munblit, D., & Offman, J. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-00940-7 · Cited by 33 · see citing works
Jang, H., Song, I.-H., & Lee, S. H. (2021) · Cancers · doi:10.3390/cancers13153811 · Cited by 31 · see citing works
Joint categorical and ordinal learning for cancer grading in pathology images
Vuong, T. T. L., Kim, K., Song, B., & Kwak, J. T. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102206 · Cited by 30 · see citing works
Yao, Z., Jin, T., Mao, B., Lu, B., Zhang, Y., Li, S., & Chen, W. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.815951 · Cited by 30 · see citing works
Patch-level Tumor Classification in Digital Histopathology Images with Domain Adapted Deep Learning
Xia, T., Kumar, A., Feng, D. D., & Kim, J. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8512353 · Cited by 29 · see citing works
Dudgeon, S. N., Wen, S., Hanna, M. G., Gupta, R., Amgad, M. T., Sheth, M., Marble, H. D., Huang, R., Herrmann, M. D., Szu, C. H., et al. (2021) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_83_20 · Cited by 29 · see citing works
Deep learning identification of stiffness markers in breast cancer
Sneider, A., Kiemen, A. L., Kim, J. H., Wu, P.-H., Habibi, M., White, M. J., Phillip, J., Gu, L., & Wirtz, D. (2022) · Biomaterials · doi:10.1016/j.biomaterials.2022.121540 · Cited by 17 · see citing works
Larey, A., Aknin, E., Daniel, N., Osswald, G. A., Caldwell, J. M., Rochman, M., Wasserman, T., Collins, M. H., Arva, N. C., Yang, G., et al. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.950728 · Cited by 28 · see citing works
Artificial Intelligence in Digital Pathology for Bladder Cancer: Hype or Hope? A Systematic Review
Khoraminia, F., Fuster, S., Kanwal, N., Olislagers, M., Engan, K., van Leenders, G. J. L. H., Stubbs, A., Akram, F., & Zuiverloon, T. C. M. (2023) · Cancers · doi:10.3390/cancers15184518 · Cited by 28 · see citing works
Tokarz, D. A., Steinbach, T., Lokhande, A., Srivastava, G., Ugalmugle, R., Co, C. A., Shockley, K. R., Singletary, E., Cesta, M. F., Thomas, H. C., et al. (2020) · Toxicologic Pathology · doi:10.1177/0192623320972614 · Cited by 27 · see citing works
Jang, H., Lee, A., Kang, J. K., Song, I. H., & Lee, S. H. (2021) · World Journal of Gastroenterology · doi:10.3748/wjg.v27.i44.7687 · Cited by 29 · see citing works
The need for measurement science in digital pathology
Romanchikova, M., Thomas, S. A., Dexter, A., Shaw, M., Partarrieau, I., Smith, N. A. S., Venton, J., Adeogun, M., Brettle, D. S., & Turpin, R. J. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100157 · Cited by 27 · see citing works
Oster, H. S., Crouch, S., Smith, A. G., Yu, G., Shrkihe, B. A., Baruch, S., Kolomansky, A., Ben‐Ezra, J. M., Naor, S., Fenaux, P., et al. (2021) · Blood Advances · doi:10.1182/bloodadvances.2020004055 · Cited by 25 · see citing works
Lee, S. H., Lee, Y., & Jang, H. (2022) · International Journal of Cancer · doi:10.1002/ijc.34251 · Cited by 26 · see citing works
Teacher-Student chain for efficient semi-supervised histology image classificationPreprint
Shaw, S., Pajak, M., Lisowska, A., Tsaftaris, S. A., & O'Neil, A. Q. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.08797 · Cited by 24 · see citing works
Aoyama, Y., Maruko, I., Kawano, T., Yokoyama, T., Ogawa, Y., Maruko, R., & Iida, T. (2021) · PLoS ONE · doi:10.1371/journal.pone.0244469 · Cited by 23 · see citing works
Guo, H., Diao, L., Zhou, X., Chen, J.-N., Zhou, Y., Fang, Q., He, Y., Dziadziuszko, R., Zhou, C., & Hirsch, F. R. (2021) · Translational Lung Cancer Research · doi:10.21037/tlcr-21-96 · Cited by 23 · see citing works
Kuklyte, J., Fitzgerald, J., Nelissen, S. R., Wei, H., Whelan, A., Power, A., Ahmad, A., Miarka, M., Gregson, M., Maxwell, M., et al. (2021) · Toxicologic Pathology · doi:10.1177/0192623320986423 · Cited by 22 · see citing works
Identifying pathological slices of gastric cancer via deep learning
Tung, C., Chang, H.-C., Yang, B.-Z., Hou, K.-J., Tsai, H.-H., Tsai, C.-Y., & Yu, P.-T. (2022) · Journal of the Formosan Medical Association · doi:10.1016/j.jfma.2022.05.004 · Cited by 22 · see citing works
The present and future role of artificial intelligence and machine learning in anesthesiology
Alexander, J. C., Romito, B. T., & Çobanoğlu, M. C. (2020) · International Anesthesiology Clinics · doi:10.1097/aia.0000000000000294 · Cited by 22 · see citing works
Prognostic markers of inflammation in endometrioid and clear cell ovarian cancer
Gallego, A., Mendiola, M., Hernando, B., Berjón, A., Cádiz, A., Chaves-Urbano, B., Heredia-Soto, V., Spagnolo, E., Hernández, A., Hardisson, D., et al. (2022) · International Journal of Gynecological Cancer · doi:10.1136/ijgc-2022-003353 · Cited by 21 · see citing works
AI in medical diagnosis: A contextualised study of patient motivations and concerns
Hajiheydari, N., Delgosha, M. S., & Saheb, T. (2025) · Social Science & Medicine · doi:10.1016/j.socscimed.2025.117850 · Cited by 21 · see citing works
Multi-omic and quantum machine learning integration for lung subtypes classification
Saggi, M. K., Bhatia, A. S., Mensah, I. K., Gowher, H., & Kais, S. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.107905 · Cited by 20 · see citing works
Deman, F., Broeckx, G., Declercq, S., Degotte, Q., Raymaekers, J., Salgado, R., & Dendooven, A. (2025) · Histopathology · doi:10.1111/his.15481 · Cited by 21 · see citing works
Perspectives in pathomics in head and neck cancer
Classe, M., Lerousseau, M., Scoazec, J., & Deutsch, É. (2021) · Current Opinion in Oncology · doi:10.1097/cco.0000000000000731 · Cited by 20 · see citing works
Smerkous, D. D., Mauer, M. S., Tøndel, C., Svarstad, E., Gübler, M.-C., Nelson, R. G., Oliveira, J. P., Sargolzaeiaval, F., & Najafian, B. (2023) · Kidney International · doi:10.1016/j.kint.2023.09.011 · Cited by 19 · see citing works
Present and Future Applications of Artificial Intelligence in Kidney Transplantation
Kotsifa, E., & Mavroeidis, V. K. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13195939 · Cited by 19 · see citing works
Development of AI-Based Diagnostic Algorithm for Nasal Bone Fracture Using Deep Learning
Jeong, Y., Jeong, C., Sung, K., Moon, G.-S., & Lim, J. (2023) · Journal of Craniofacial Surgery · doi:10.1097/scs.0000000000009856 · Cited by 19 · see citing works
Liu, X., Kong, Y., Qian, Y., Guo, H., Zhao, L., Wang, H., Xu, K., Ye, L., Liu, Y., Lu, H., et al. (2024) · Translational Oncology · doi:10.1016/j.tranon.2024.102143 · Cited by 19 · see citing works
Schuettfort, V. M., Pradère, B., Rink, M., Compérat, É., & Shariat, S. F. (2020) · Current Opinion in Urology · doi:10.1097/mou.0000000000000813 · Cited by 18 · see citing works
Computational pathology in ovarian cancer
Orsulic, S., John, J., Walts, A., & Gertych, A. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.924945 · Cited by 12 · see citing works
Zhang, T., Chen, J., Lü, Y., Yang, X., & Ouyang, Z. (2022) · PLoS ONE · doi:10.1371/journal.pone.0273355 · Cited by 18 · see citing works
Başak, K., Ozyoruk, K. B., & Demir, D. (2023) · Turkish Journal of Pathology · doi:10.5146/tjpath.2023.01601 · Cited by 18 · see citing works
Swillens, J. E. M., Nagtegaal, I. D., Engels, S., Lugli, A., Hermens, R., & van der Laak, J. A. W. M. (2023) · Oncogene · doi:10.1038/s41388-023-02797-1 · Cited by 18 · see citing works
- Authors
- Serag, A., Ion-Margineanu, A., Qureshi, H., McMillan, R., Saint Martin, M.-J., Diamond, J., O'Reilly, P., & Hamilton, P.
- Affiliations
- Philips (United Kingdom) (GB)
- Corresponding author
- Hamilton, Peter
- Venue
- Frontiers in Medicine
- Publisher
- Frontiers Media SA
- Published
- 2019-10-01
- Volume
- 6
- Pages
- 185-185
- ISSN
- 2296-858X (ISSN-L)
- PMC
- PMC6779702
- OpenAlex
- W2965481926
- Cited by
- 274
- References
- 94
- Citations by year
- 2026: 22 · 2025: 43 · 2024: 35 · 2023: 46 · 2022: 43 · 2021: 51 · 2020: 33 · 2018: 1
- FWCI
- 2.06
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- artificial intelligence in pathology, deep learning, digital pathology, computational pathology, cell imaging, diagnostic consistency
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