Cited by 274AI in cancer detectionArtificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging

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Translational AI and Deep Learning in Diagnostic Pathology

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Serag, Ahmed, Ion-Margineanu, Adrian, Qureshi, Hammad, McMillan, Ryan, Saint Martin, Marie-Judith, Diamond, Jim, O'Reilly, Paul, Hamilton, Peter·2019·Frontiers in Medicine

DOI 10.3389/fmed.2019.00185PMC PMC6779702

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Abstract

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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Figure 1 U-Net architecture for semantic segmentation, comprising encoder (downsampling), and decoder (upsampling) sections, and showing the skip connections between layers (in yellow).CC BY · PubMed Central

Figure 2 GANs, Generative adversarial networks (GANs) are deep neural network architectures comprised of two networks (generator and discriminator), opposing one against the other (thus the “adversarial”). The generator takes in random numbers and returns an image. This generated image is fed into the discriminator alongside a stream of images taken from the actual, ground-truth dataset.CC BY · PubMed Central

Figure 3 Unsupervised Learning, unsupervised anomaly detection framework. Generative adversarial training is performed on healthy data and testing is performed on unseen data.CC BY · PubMed Central

Figure 4 PD-L1 imaging in lung cancer. Deep learning can be used to identify and distinguish positive | negative tumor cells and positive | negative inflammatory cells.CC BY · PubMed Central

Figure 5 Illustrates the current workflow in molecular research and diagnostics. Solid tumor analysis is commonly derived from FFPE block and H&E tissue section as guide for tumor content (far left). The figure shows the need for annotation and macrodissection and the importance of tumor purity from FFPE samples for molecular profiling. Digital pathology can automate the annotation and measurement of tumor cells in H&E—providing a more objective, reliable platform for molecular pathology.CC BY · PubMed Central

Figure 6 Automated identification of colorectal tumor in H&E tissue samples using deep learning networks, showing heatmap of tumor regions (Left) and automatically generated macrodissection boundary (Right) with a product called TissueMark1.CC BY · PubMed Central

Figure 7 Automated analysis of cellular content in H&E using deep learning in TissueMark1. Here tumor (red) and non-tumor cells (green) can be distinguished, annotated for visual inspection and counted to reach more precise qualititive measures of % tumor across entire whole slide H&E scans in lung, colon, melanoma, breast, and prostate tissue sections.CC BY · PubMed Central

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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
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
Evaluation of Factors Related to Late Recurrence - Later than 10 Years after the Initial Treatment - in Primary Breast Cancer
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
Comprehensive molecular portraits of human breast tumours
The Cancer Genome Atlas Network (2012) · Nature · doi:10.1038/nature11412 · Cited by 12,129
The Ki-67 protein: From the known and the unknown
Scholzen, T. E., & Gerdes, J. (2000) · Journal of Cellular Physiology · doi:10.1002/(sici)1097-4652(200003)182:3 · Cited by 4,707
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
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
Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
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
The 2014 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma
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
Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
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
The 2005 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma
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
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
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
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 1,920
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
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 1,645
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 1,574
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
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
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
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 1,160
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
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
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
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
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
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 1,044
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
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 1,014
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 755
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
From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge
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
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 514
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
Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer
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
Artificial Intelligence in Drug Discovery and Development
Agrawal, P. (2018) · Journal of Pharmacovigilance · doi:10.4172/2329-6887.1000e173 · Cited by 493
Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer
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
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
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 472
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 461
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 441
Whole Slide Imaging Versus Microscopy for Primary Diagnosis in Surgical Pathology: A Multicenter Blinded Randomized Noninferiority Study of 1992 Cases (Pivotal Study)
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
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 371
Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology
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 319
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 313
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
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
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
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 215
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
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
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
PD-L1 diagnostic tests: a systematic literature review of scoring algorithms and test-validation metrics
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
Beyond Classification: Structured Regression for Robust Cell Detection Using Convolutional Neural Network
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 203
HER 2 challenge contest: a detailed assessment of automated HER 2 scoring algorithms in whole slide images of breast cancer tissues
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
Analytical validation of a standardized scoring protocol for Ki67: phase 3 of an international multicenter collaboration
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
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
Ki-67 and outcome in clinically localised prostate cancer: analysis of conservatively treated prostate cancer patients from the Trans-Atlantic Prostate Group study
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
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
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 123
Deep Semi Supervised Generative Learning for Automated Tumor Proportion Scoring on NSCLC Tissue Needle Biopsies
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 115
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 112
A Prospective, Multi-Institutional Diagnostic Trial to Determine Pathologist Accuracy in Estimation of Percentage of Malignant Cells
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
The Potential Impact of Reproducibility of Gleason Grading in Men With Early Stage Prostate Cancer Managed by Active Surveillance: A Multi-Institutional Study
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
Standardized Ki67 Diagnostics Using Automated Scoring—Clinical Validation in the GeparTrio Breast Cancer Study
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
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
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 94
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
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 89
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
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 80
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 79
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
Prognostic value of automated KI67 scoring in breast cancer: a centralised evaluation of 8088 patients from 10 study groups
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
Quantification of diverse subcellular immunohistochemical markers with clinicobiological relevancies: validation of a new computer‐assisted image analysis procedure
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
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
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
Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score
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 69
Automated Tumour Recognition and Digital Pathology Scoring Unravels New Role for PD-L1 in Predicting Good Outcome in ER-/HER2+ Breast Cancer
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 63
The Pathologist Workforce in the United States: II. An Interactive Modeling Tool for Analyzing Future Qualitative and Quantitative Staffing Demands for Services
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
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
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
Critical Appraisal of Programmed Death Ligand 1 Reflex Diagnostic Testing: Current Standards and Future Opportunities
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
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 48
Relevant impact of central pathology review on nodal classification in individual breast cancer patients
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
Automated analysis of immunohistochemistry images identifies candidate location biomarkers for cancers
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 45
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
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 42
Interobserver reproducibility of Gleason grading of prostatic adenocarcinoma among general pathologists
Singh, R., Agashe, S., Gosavi, A., & Sulhyan, K. (2011) · Indian Journal of Cancer · doi:10.4103/0019-509x.92277 · Cited by 33
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 30
Synergistic Tissue Counterstaining and Image Segmentation Techniques for Accurate, Quantitative Immunohistochemistry
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
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
Large scale digital prostate pathology image analysis combining feature extraction and deep neural networkPreprint
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
Automated comparison of protein subcellular location patterns between images of normal and cancerous tissues
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
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
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
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
A benchmark for comparing precision medicine methods in thyroid cancer diagnosis using tissue microarrays
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
Automating proliferation rate estimation from Ki-67 histology images
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[Regular Paper] MVPNets: Multi-viewing Path Deep Learning Neural Networks for Magnification Invariant Diagnosis in Breast Cancer
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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
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
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
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
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
Multi-resolution tile-based follicle detection using color and textural information of follicular lymphoma IHC slides
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
Corrigendum to “Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge” [Medical Image Analysis, 54 (2019) 111--121]
Veta, M. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.05.008 · Cited by 3

Research works printed but not linked: 6

Press Announcements - FDA Allows Marketing of First Whole Slide Imaging System for Digital Pathology2017
Detection of mitosis and evaluation of nuclear atypia score in breast cancer histological images RouxL 22nd International Conference on Pattern Recognition 2014. MITOS-ATYPIA Contest.2014
Mitosis detection in breast cancer histology images via deep cascaded networks11606 ChenH DouQ WangX QinJ HengP-A AAAI PressProceedings of the Thirtieth AAAI Conference on Artificial Intelligence2016
Automated segmentation of epithelial tissue in prostatectomy slides using deep learning BultenW LitjensGJS Hulsbergen-van de KaaCA van der LaakJ Proceedings Medical Imaging 2018: Digital Pathology.2018
Towards grading gleason score using generically trained deep convolutional neural networks11637 KallenH MolinJ HeydenA LundstromC AstromK In: 2016 IEEE 13th Int Symp on Biomed Im (ISBI).2016
Automatic lymphocyte detection on gastric cancer ihc images using deep learning2004 GarciaE HermozaR CastanonCB CanoL CastilloM CastannedaC IEEE 30th International Symposium on Computer Based Medical Systems (CBMS)2017

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See all 274

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
ORCID: Serag, Ahmed
Hamilton, Peter
Venue: Frontiers in Medicine
Publisher: Frontiers Media SA
Published: 2019-10-01
Volume: 6
Pages: 185-185
ISSN: 2296-858X (ISSN-L)
DOI: 10.3389/fmed.2019.00185
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
FWCI: 2.06
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
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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