Validation of digital pathology imaging for primary histopathological diagnosis

Histopathological Image Analysis: A Review

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Validating Whole Slide Imaging for Diagnostic Purposes in Pathology: Guideline from the College of American Pathologists Pathology and Laboratory Quality Center

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Validation of whole slide imaging for primary diagnosis in surgical pathology

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Digital slide and virtual microscopy based routine and telepathology evaluation of routine gastrointestinal biopsy specimens

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Telepathology diagnosis by means of digital still images: An international validation study

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Epstein-Barr virus in B-cell lymphomas associated with chronic suppurative inflamation

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The addition of an audiocassette recording of a consultation to written recommendations for patients with advanced cancer

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Concordance between whole-slide imaging and light microscopy for routine surgical pathology

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Clinical Examination and Validation of Primary Diagnosis in Anatomic Pathology Using Whole Slide Digital Images

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Ovarian Frozen Section Diagnosis: Use of Whole-Slide Imaging Shows Excellent Correlation Between Virtual Slide and Original Interpretations in a Large Series of Cases

Fallon, M. A., Wilbur, D. C., & Prasad, M. L. (2010) · Archives of Pathology & Laboratory Medicine · doi:10.5858/2009-0320-oa.1 · Cited by 79 · see citing works

Evaluation of whole slide image immunohistochemistry interpretation in challenging prostate needle biopsies

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Multi-Institutional Comparison of Whole Slide Digital Imaging and Optical Microscopy for Interpretation of Hematoxylin-Eosin–Stained Breast Tissue Sections

Krishnamurthy, S., Mathews, K., McClure, S., Murray, M. P., Gilcrease, M. Z., Albarracin, C. T., Spinosa, J. C., Chang, B., Ho, J., Holt, J., et al. (2013) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2012-0437-oa · Cited by 71 · see citing works

Virtual 3D Microscopy Using Multiplane Whole Slide Images in Diagnostic Pathology

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Whole slide imaging diagnostic concordance with light microscopy for breast needle biopsies

Campbell, W. S., Hinrichs, S. H., Lele, S. M., Baker, J. J., Lazenby, A. J., Talmon, G. A., Smith, L. M., & West, W. W. (2014) · Human Pathology · doi:10.1016/j.humpath.2014.04.007 · Cited by 63 · see citing works

Superiority of virtual microscopy versus light microscopy in transplantation pathology

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Validation of whole slide imaging in the primary diagnosis of gynaecological pathology in a University Hospital

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A feasibility study of virtual slides in surgical pathology in China

Li, X., Liu, J., Xu, H., Gong, E., McNutt, M. A., Li, F., Anderson, V., & Gu, J. (2007) · Human Pathology · doi:10.1016/j.humpath.2007.04.019 · Cited by 50 · see citing works

Validation of diagnostic accuracy using digital slides in routine histopathology

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Observer agreement comparing the use of virtual slides with glass slides in the pathology review component of the POSH breast cancer cohort study

Shaw, E. C., Hanby, A. M., Wheeler, K., Shaaban, A. M., Poller, D. N., Barton, S. J., Treanor, D. E., Fulford, L. G., Walker, R. A., Ryan, D., et al. (2012) · Journal of Clinical Pathology · doi:10.1136/jclinpath-2011-200369 · Cited by 31 · see citing works

Telepathology in a routine clinical environment: implementation and accuracy of diagnosis by robotic microscopy in a one‐stop breast clinic

Singh, N., Akbar, N., Sowter, C., Lea, K. G., & Wells, C. A. (2002) · The Journal of Pathology · doi:10.1002/path.1049 · Cited by 28 · see citing works

A questionnaire-based survey of errors in diagnostic histopathology throughout the United Kingdom.

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Robotic telepathology: efficacy and usability in pulmonary pathology

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Evaluation of immunohistochemical staining using whole‐slide imaging for HER2 scoring of breast cancer in comparison with real glass slides

Kondo, Y., Iijima, T., & Noguchi, M. (2012) · Pathology International · doi:10.1111/j.1440-1827.2012.02847.x · Cited by 23 · see citing works

Departmental audit in surgical anatomical pathology

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Departmental Audit in Histopathology

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AI in Medical Imaging Informatics: Current Challenges and Future Directions

Panayides, A. S., Amini, A., Filipovic, N. D., Sharma, A., Tsaftaris, S. A., Young, A., Foran, D., Do, N., Golemati, S., Kurc, T., et al. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2991043 · Cited by 713 · see citing works

Suboptimal reliability of liver biopsy evaluation has implications for randomized clinical trials

Davison, B. A., Harrison, S. A., Cotter, G., Alkhouri, N., Sanyal, A., Edwards, C., Colca, J. R., Iwashita, J., Koch, G. G., & Dittrich, H. C. (2020) · Journal of Hepatology · doi:10.1016/j.jhep.2020.06.025 · Cited by 448 · see citing works

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 · see citing works

Digital Pathology: Advantages, Limitations and Emerging Perspectives

Jahn, S. W., Plass, M., & Moinfar, F. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9113697 · Cited by 351 · see citing works

Deep Learning for Whole Slide Image Analysis: An Overview

Dimitriou, N., Arandjelović, O., & Caie, P. D. (2019) · Frontiers in Medicine · doi:10.3389/fmed.2019.00264 · Cited by 334 · see citing works

Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study

Bilal, M., Raza, S. E. A., Azam, A., Graham, S., Ilyas, M., Cree, I. A., Snead, D., Minhas, F., & Rajpoot, N. M. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00180-1 · Cited by 299 · see citing works

Artificial Intelligence in Lung Cancer Pathology Image Analysis

Wang, S., Yang, D. M., Rong, R., Zhan, X., Fujimoto, J., Liu, H., Minna, J. D., Wistuba, I. I., Xie, Y., & Xiao, G. (2019) · Cancers · doi:10.3390/cancers11111673 · Cited by 293 · see citing works

Twenty Years of Digital Pathology: An Overview of the Road Travelled, What is on the Horizon, and the Emergence of Vendor-Neutral Archives

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 · see citing works

Digital pathology in clinical use: where are we now and what is holding us back?

Griffin, J., & Treanor, D. E. (2016) · Histopathology · doi:10.1111/his.12993 · Cited by 265 · see citing works

Deep learning for colon cancer histopathological images analysis

Hamida, A. B., Devanne, M., Weber, J., Truntzer, C., Dérangère, V., Ghiringhelli, F., Forestier, G., & Wemmert, C. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104730 · Cited by 252 · see citing works

Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice

Colling, R. T., Pitman, H., Oien, K. A., Rajpoot, N., Macklin, P. S., CM‐Path AI in Histopathology Working Group, Snead, D. R. J., Sackville, T., & Verrill, C. L. (2019) · The Journal of Pathology · doi:10.1002/path.5310 · Cited by 238 · see citing works

Validating Whole Slide Imaging Systems for Diagnostic Purposes in Pathology

Evans, A. J., Brown, R. W., Bui, M. M., Chlipala, E. A., Lacchetti, C., Milner, D. A., Pantanowitz, L., Parwani, A. V., Reid, K., Riben, M. W., et al. (2021) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2020-0723-cp · Cited by 228 · see citing works

Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features

Qaiser, T., Tsang, Y., Taniyama, D., Sakamoto, N., Nakane, K., Epstein, D. B. A., & Rajpoot, N. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.03.014 · Cited by 221 · see citing works

Whole slide imaging equivalency and efficiency study: experience at a large academic center

Hanna, M. G., Reuter, V. E., Hameed, M., Tan, L. K., Chiang, S., Sigel, C., Hollmann, T. J., Giri, D. D., Samboy, J., Moradel, C., et al. (2019) · Modern Pathology · doi:10.1038/s41379-019-0205-0 · Cited by 215 · see citing works

Validation of a digital pathology system including remote review during the COVID-19 pandemic

Hanna, M. G., Reuter, V. E., Ardon, O., Kim, D., Sirintrapun, S. J., Schüffler, P. J., BUSAM, K. J., Sauter, J. L., Brogi, E., Tan, L. K., et al. (2020) · Modern Pathology · doi:10.1038/s41379-020-0601-5 · Cited by 187 · see citing works

A Novel Digital Score for Abundance of Tumour Infiltrating Lymphocytes Predicts Disease Free Survival in Oral Squamous Cell Carcinoma

Shaban, M., Khurram, S. A., Fraz, M. M., Alsubaie, N. M., Masood, I., Mushtaq, S., Hassan, M., Loya, A., & Rajpoot, N. (2019) · Scientific Reports · doi:10.1038/s41598-019-49710-z · Cited by 175 · see citing works

Cellular community detection for tissue phenotyping in colorectal cancer histology images

Javed, S., Mahmood, A., Fraz, M. M., Koohbanani, N. A., Benes, K., Tsang, Y., Hewitt, K. J., Epstein, D., Snead, D. R. J., & Rajpoot, N. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101696 · Cited by 171 · see citing works

Whole Slide Imaging: Technology and Applications

Hanna, M. G., Parwani, A. V., & Sirintrapun, S. J. (2020) · Advances in Anatomic Pathology · doi:10.1097/pap.0000000000000273 · Cited by 171 · see citing works

US Food and Drug Administration Approval of Whole Slide Imaging for Primary Diagnosis: A Key Milestone Is Reached and New Questions Are Raised

Evans, 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 · see citing works

Integrating digital pathology into clinical practice

Hanna, M. G., Ardon, O., Reuter, V. E., Sirintrapun, S. J., England, C., Klimstra, D. S., & Hameed, M. (2021) · Modern Pathology · doi:10.1038/s41379-021-00929-0 · Cited by 157 · see citing works

Emerging Themes in Image Informatics and Molecular Analysis for Digital Pathology

Bhargava, R., & Madabhushi, A. (2016) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-112415-114722 · Cited by 146 · see citing works

Evolution of Diagnostic Methods for Helicobacter pylori Infections: From Traditional Tests to High Technology, Advanced Sensitivity and Discrimination Tools

Cardos, I. A., Maghiar, A., Zaha, D. C., Pop, O. L., Fritea, L., Miere, F. G., & Cavalu, S. D. (2022) · Diagnostics · doi:10.3390/diagnostics12020508 · Cited by 132 · see citing works

Fast and scalable search of whole-slide images via self-supervised deep learning

Chen, C., Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Schaumberg, A. J., & Mahmood, F. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00929-8 · Cited by 130 · see citing works

The Emergence of Pathomics

Gupta, R., Kurç, T., Sharma, A., Almeida, J. S., & Saltz, J. H. (2019) · Current Pathobiology Reports · doi:10.1007/s40139-019-00200-x · Cited by 129 · see citing works

Best Practice Recommendations for the Implementation of a Digital Pathology Workflow in the Anatomic Pathology Laboratory by the European Society of Digital and Integrative Pathology (ESDIP)

Fraggetta, F., L’Imperio, V., Ameisen, D., Carvalho, R., Leh, S. M., Kiehl, T., Şerbănescu, M.-S., Racoceanu, D., Mea, V. D., Polónia, A., et al. (2021) · Diagnostics · doi:10.3390/diagnostics11112167 · Cited by 130 · see citing works

Introduction to digital pathology and computer-aided pathology

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Application of Artificial Intelligence in Pathology: Trends and Challenges

Kim, I., Kang, K., Song, Y., & Kim, T. J. (2022) · Diagnostics · doi:10.3390/diagnostics12112794 · Cited by 126 · see citing works

ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network

Wang, S., Wang, T., Yang, L., Yang, D. M., Fujimoto, J., Yi, F., Luo, X., Yang, Y., Yao, B., Lin, S.-Y., et al. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.10.033 · Cited by 117 · see citing works

Routine Digital Pathology Workflow: The Catania Experience

Fraggetta, F., Garozzo, S., Zannoni, G. F., Pantanowitz, L., & Rossi, E. D. (2017) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_58_17 · Cited by 111 · see citing works

Multifunctional nanoparticle developments in cancer diagnosis and treatment

Parvanian, S., Mostafavi, S. M., & Aghashiri, M. (2016) · Sensing and Bio-Sensing Research · doi:10.1016/j.sbsr.2016.08.002 · Cited by 109 · see citing works

A narrative review of digital pathology and artificial intelligence: focusing on lung cancer

Sakamoto, T., Furukawa, T., Lami, K., Pham, H. H. N., Uegami, W., Kuroda, K., Kawai, M., Sakanashi, H., Cooper, L. A. D., Bychkov, A., et al. (2020) · Translational Lung Cancer Research · doi:10.21037/tlcr-20-591 · Cited by 106 · see citing works

Digital Whole Slide Imaging Compared With Light Microscopy for Primary Diagnosis in Surgical Pathology

Borowsky, A. D., Glassy, E. F., Wallace, W. D., Kallichanda, N. S., Behling, C. A., Miller, D. V., Oswal, H. N., Feddersen, R. M., Bakhtar, O. R., Mendoza, A., et al. (2020) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2019-0569-oa · Cited by 105 · see citing works

Implementation of Whole Slide Imaging for Clinical Purposes: Issues to Consider From the Perspective of Early Adopters

Evans, A. J., Salama, M. E., Henricks, W. H., & Pantanowitz, L. (2017) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2016-0074-oa · Cited by 104 · see citing works

Classification and Disease Localization in Histopathology Using Only Global Labels: A Weakly-Supervised ApproachPreprint

Courtiol, P., Tramel, E. W., Sanselme, M., & Wainrib, G. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.02212 · Cited by 99 · see citing works

Advancements in AI based healthcare techniques with FOCUS ON diagnostic techniques

Kalra, N., Verma, P., & Verma, S. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108917 · Cited by 95 · see citing works

Learning Where to See: A Novel Attention Model for Automated Immunohistochemical Scoring

Qaiser, T., & Rajpoot, N. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2907049 · Cited by 94 · see citing works

Diagnostic concordance and discordance in digital pathology: a systematic review and meta-analysis

Azam, A. S., Miligy, I. M., Kimani, P., Maqbool, H., Hewitt, K. J., Rajpoot, N., & Snead, D. R. J. (2020) · Journal of Clinical Pathology · doi:10.1136/jclinpath-2020-206764 · Cited by 83 · see citing works

Integrated digital pathology at scale: A solution for clinical diagnostics and cancer research at a large academic medical center

Schüffler, P. J., Geneslaw, L., Yarlagadda, D. V. K., Hanna, M. G., Samboy, J., Stamelos, E., Vanderbilt, C., Philip, J., Jean, M.-H., Corsale, L., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab085 · Cited by 83 · see citing works

Region of interest (ROI) selection using vision transformer for automatic analysis using whole slide images

Hossain, M. S., Shahriar, G. M., Syeed, M. M. M., Uddin, M. F., Hasan, M., Shingla, S., & Advani, S. H. (2023) · Scientific Reports · doi:10.1038/s41598-023-38109-6 · Cited by 82 · see citing works

Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images

Graham, S., Epstein, D. B. A., & Rajpoot, N. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3013246 · Cited by 80 · see citing works

Digital pathology for the primary diagnosis of breast histopathological specimens: an innovative validation and concordance study on digital pathology validation and training

Williams, B. J., Hanby, A. M., Millican-Slater, R. A., Nijhawan, A., Verghese, E. T., & Treanor, D. E. (2017) · Histopathology · doi:10.1111/his.13403 · Cited by 78 · see citing works

A review for cervical histopathology image analysis using machine vision approaches

Li, C., Chen, H., Li, X., Xu, N., Hu, Z., Xue, D., Qi, S., Ma, H., Zhang, L., & Sun, H. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09808-7 · Cited by 78 · see citing works

Ki-67 assessment of pancreatic neuroendocrine neoplasms: Systematic review and meta-analysis of manual vs. digital pathology scoring

Luchini, C., Pantanowitz, L., Adsay, V. N., Asa, S. L., Antonini, P., Girolami, I., Veronese, N., Nottegar, A., Cingarlini, S., Landoni, L., et al. (2022) · Modern Pathology · doi:10.1038/s41379-022-01055-1 · Cited by 77 · see citing works

Artificial Intelligence in Brain Tumour Surgery—An Emerging Paradigm

Williams, S. C., Horsfall, H. L., Funnell, J. P., HANRAHAN, J. G., Khan, D. Z., Muirhead, W. R., Stoyanov, D. V., & Marcus, H. J. (2021) · Cancers · doi:10.3390/cancers13195010 · Cited by 77 · see citing works

Enabling digital pathology in the diagnostic setting: navigating through the implementation journey in an academic medical centre

Cheng, C. L., Azhar, R., Sng, S. H. A., Chua, Y. Q., Hwang, J. S. G., Chin, J. P. F., Seah, W. K., Loke, J. C. L., Ang, R. H. L., & Tan, P. H. (2016) · Journal of Clinical Pathology · doi:10.1136/jclinpath-2015-203600 · Cited by 76 · see citing works

A Systematic Analysis of Discordant Diagnoses in Digital Pathology Compared With Light Microscopy

Williams, B. J., DaCosta, P., Goacher, E., & Treanor, D. E. (2017) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2016-0494-oa · Cited by 71 · see citing works

What Is New in Helicobacter pylori Diagnosis. An Overview

Dore, M. P., & Pes, G. M. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10102091 · Cited by 69 · see citing works

Enterprise Implementation of Digital Pathology: Feasibility, Challenges, and Opportunities

Hartman, D., Pantanowitz, L., McHugh, J., Piccoli, A. L., O'Leary, M. M., & Lauro, G. R. (2017) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-017-9946-9 · Cited by 67 · see citing works

The performance of digital microscopy for primary diagnosis in human pathology: a systematic review

Araújo, A. L. D., Arboleda, L. P. A., Palmier, N. R., Fonsêca, J. M., de Pauli Paglioni, M., Gomes-Silva, W., Ribeiro, A. C. P., Brandão, T. B., Simonato, L. E., Speight, P. M., et al. (2019) · Virchows Archiv · doi:10.1007/s00428-018-02519-z · Cited by 64 · see citing works

A Survival Guide for the Rapid Transition to a Fully Digital Workflow: The “Caltagirone Example”

Fraggetta, F., Caputo, A., Guglielmino, R., Pellegrino, M. G., Runza, G., & L’Imperio, V. (2021) · Diagnostics · doi:10.3390/diagnostics11101916 · Cited by 64 · see citing works

Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks

Bándi, P., Balkenhol, M. C. A., van Ginneken, B., van der Laak, J. A. W. M., & Litjens, G. J. S. (2019) · PeerJ · doi:10.7717/peerj.8242 · Cited by 61 · see citing works

Histopathologist features predictive of diagnostic concordance at expert level among a large international sample of pathologists diagnosing Barrett’s dysplasia using digital pathology

van der Wel, M. J., Coleman, H. G., Bergman, J. J. G. H. M., Jansen, M., Meijer, S. L., & on behalf of the BOLERO working group (2019) · Gut · doi:10.1136/gutjnl-2019-318985 · Cited by 61 · see citing works

Whole‐slide imaging at primary pathological diagnosis: Validation of whole‐slide imaging‐based primary pathological diagnosis at twelve Japanese academic institutes

Tabata, K., Mori, I., Sasaki, T., Itoh, T., Shiraishi, T., Yoshimi, N., Maeda, I., Harada, O., Taniyama, K., Taniyama, D., et al. (2017) · Pathology International · doi:10.1111/pin.12590 · Cited by 60 · see citing works

Digital Pathology and Artificial Intelligence Applications in Pathology

Go, H. (2022) · Brain Tumor Research and Treatment · doi:10.14791/btrt.2021.0032 · Cited by 59 · see citing works

Current Developments of Artificial Intelligence in Digital Pathology and Its Future Clinical Applications in Gastrointestinal Cancers

Wong, A. N. N., He, Z., Leung, K. L., To, C. C. K., Wong, C. Y., Wong, S. C. C., Yoo, J. S., Chan, C. K. R., Chan, A. Z., Lacambra, M. D., et al. (2022) · Cancers · doi:10.3390/cancers14153780 · Cited by 59 · see citing works

HEROHE Challenge: Predicting HER2 Status in Breast Cancer from Hematoxylin–Eosin Whole-Slide Imaging

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Digital Pathology for Better Clinical Practice

Hijazi, A., Bifulco, C. B., Baldin, P., & Galon, J. (2024) · Cancers · doi:10.3390/cancers16091686 · Cited by 56 · see citing works

Comparison of different methods for tissue segmentation in histopathological whole-slide images

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A novel method for tissue segmentation in high-resolution H&E-stained histopathological whole-slide images

Kłeczek, P., Jaworek-Korjakowska, J., & Gorgoń, M. (2019) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2019.101686 · Cited by 56 · see citing works

Usability of deep learning and H&E images predict disease outcome-emerging tool to optimize clinical trials

Qaiser, T., Lee, C.-Y., Vandenberghe, M. E., Yeh, J., Gavrielides, M. A., Hipp, J., Scott, M. L. J., & Reischl, J. (2022) · npj Precision Oncology · doi:10.1038/s41698-022-00275-7 · Cited by 55 · see citing works

Novel digital signatures of tissue phenotypes for predicting distant metastasis in colorectal cancer

Sirinukunwattana, K., Snead, D. R. J., Epstein, D. B. A., Aftab, Z., Mujeeb, I., Tsang, Y. W., Cree, I. A., & Rajpoot, N. (2018) · Scientific Reports · doi:10.1038/s41598-018-31799-3 · Cited by 52 · see citing works

Display Characteristics and Their Impact on Digital Pathology: A Current Review of Pathologists’ Future “Microscope”

Abel, J. T., Ouillette, P. D., Williams, C. L., Blau, J., Cheng, J., Yao, K., Lee, W. Y., Cornish, T. C., Balis, U. J., & McClintock, D. S. (2020) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_38_20 · Cited by 49 · see citing works

Digital pathology systems enabling quality patient care

Hanna, M. G., & Ardon, O. (2023) · Genes Chromosomes and Cancer · doi:10.1002/gcc.23192 · Cited by 48 · see citing works

Digital pathology: semper ad meliora

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Multiplex Cellular Communities in Multi-Gigapixel Colorectal Cancer Histology Images for Tissue Phenotyping

Javed, S., Mahmood, A., Werghi, N., Benes, K., & Rajpoot, N. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3023795 · Cited by 48 · see citing works

Commentary

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Whole‐slide margin control through deep learning in Mohs micrographic surgery for basal cell carcinoma

van Zon, M. C. M., van der Waa, J. D., Veta, M., & Krekels, G. A. M. (2021) · Experimental Dermatology · doi:10.1111/exd.14306 · Cited by 46 · see citing works

Semi-Supervised Nests of Melanocytes Segmentation Method Using Convolutional Autoencoders

Kucharski, D., Kłeczek, P., Jaworek-Korjakowska, J., Dyduch, G., & Gorgoń, M. (2020) · Sensors · doi:10.3390/s20061546 · Cited by 45 · see citing works

The human‐in‐the‐loop: an evaluation of pathologists’ interaction with artificial intelligence in clinical practice

Bodén, A., Molin, J., Garvin, S., West, R. A., Lundström, C. F., & Treanor, D. E. (2021) · Histopathology · doi:10.1111/his.14356 · Cited by 44 · see citing works

Technical and Diagnostic Issues in Whole Slide Imaging Published Validation Studies

Rizzo, P. C., Girolami, I., Marletta, S., Pantanowitz, L., Antonini, P., Brunelli, M., Santonicco, N., Vacca, P., Tumino, N., Moretta, L., et al. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.918580 · Cited by 43 · see citing works

Computer-Aided Pathologic Diagnosis of Nasopharyngeal Carcinoma Based on Deep Learning

Diao, S., Hou, J., Yu, H. Q., Zhao, X., Sun, Y.-K., Lambo, R. L., Xie, Y., Liu, L., Qin, W., & Luo, W. (2020) · American Journal Of Pathology · doi:10.1016/j.ajpath.2020.04.008 · Cited by 43 · see citing works

Conventional Microscopical versus Digital Whole-Slide Imaging-Based Diagnosis of Thin-Layer Cervical Specimens: A Validation Study

Bongaerts, O., Clevers, C., Debets, M., Paffen, D., Senden, L., Rijks, K., Ruiten, L., Go, D. M. D. S., van Diest, P. J., & Nap, M. (2018) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_28_18 · Cited by 42 · see citing works

Impact of stain normalization and patch selection on the performance of convolutional neural networks in histological breast and prostate cancer classification

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An update on the validation of whole slide imaging systems following FDA approval of a system for a routine pathology diagnostic service in the United States

Boyce, B. F. (2017) · Biotechnic & Histochemistry · doi:10.1080/10520295.2017.1355476 · Cited by 41 · see citing works

Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology

Mi, H., Sivagnanam, S., Ho, W. J., Zhang, S., Bergman, D. R., Deshpande, A. M., Baras, A. S., Jaffee, E. M., Coussens, L. M., Fertig, E. J., et al. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae421 · Cited by 38 · see citing works

Future-proofing pathology part 2: building a business case for digital pathology

Williams, B. J., Bottoms, D., Clark, D., & Treanor, D. E. (2018) · Journal of Clinical Pathology · doi:10.1136/jclinpath-2017-204926 · Cited by 37 · see citing works

Recommendations for pathologic practice using digital pathology: consensus report of the Korean Society of Pathologists

Chong, Y., Kim, D. C., Jung, C. K., Kim, D.-C., Song, S. Y., Joo, H. J., & Yi, S. Y. (2020) · Journal of Pathology and Translational Medicine · doi:10.4132/jptm.2020.08.27 · Cited by 37 · see citing works

An empirical analysis of machine learning frameworks for digital pathology in medical science

Sangeetha, S., Dhaya, R., Shah, D. T., Dharanidharan, R., & Reddy, K. P. S. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1767/1/012031 · Cited by 37 · see citing works

Validation of Digital Microscopy Compared With Light Microscopy for the Diagnosis of Canine Cutaneous Tumors

Bertram, C., Gurtner, C., Dettwiler, M., Kershaw, O., Dietert, K., Pieper, L., Pischon, H., Gruber, A. D., & Klopfleisch, R. (2018) · Veterinary Pathology · doi:10.1177/0300985818755254 · Cited by 35 · see citing works

Validation of mitotic cell quantification via microscopy and multiple whole-slide scanners

Tabata, K., Uraoka, N., Benhamida, J. K., Hanna, M. G., Sirintrapun, S. J., Gallas, B. D., Gong, Q., Aly, R. G. M., Emoto, K., Matsuda, K. M., et al. (2019) · Diagnostic Pathology · doi:10.1186/s13000-019-0839-8 · Cited by 35 · see citing works

Why is digital pathology in cytopathology lagging behind surgical pathology?

Hanna, M. G., & Pantanowitz, L. (2017) · Cancer Cytopathology · doi:10.1002/cncy.21855 · Cited by 35 · see citing works

Validation of Digital Pathology for Primary Histopathological Diagnosis of Routine, Inflammatory Dermatopathology Cases

Lee, J. J., Jedrych, J. J., Pantanowitz, L., & Ho, J. (2017) · American Journal of Dermatopathology · doi:10.1097/dad.0000000000000888 · Cited by 34 · see citing works

Ethical issues in computational pathology

Sorell, T., Rajpoot, N., & Verrill, C. L. (2021) · Journal of Medical Ethics · doi:10.1136/medethics-2020-107024 · Cited by 34 · see citing works

Use of digital pathology and artificial intelligence for the diagnosis of Helicobacter pylori in gastric biopsies

Liscia, D. S., D’Andrea, M., Biletta, E., Bellis, D., Demo, K., Ferrero, F., Petti, A., Butinar, R., D’Andrea, E., & Davini, G. (2022) · Pathologica · doi:10.32074/1591-951x-751 · Cited by 33 · see citing works

Verification and Validation of Digital Pathology (Whole Slide Imaging) for Primary Histopathological Diagnosis: All Wales Experience

Babawale, M. O., Gunavardhan, A., Walker, J. D., Corfield, T., Huey, P., Savage, A., Bansal, A., Atkinson, M., Abdelsalam, H., Raweily, E., et al. (2021) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_55_20 · Cited by 32 · see citing works

WHO/ISUP grading of clear cell renal cell carcinoma and papillary renal cell carcinoma; validation of grading on the digital pathology platform and perspectives on reproducibility of grade

Browning, L., Colling, R. T., & Verrill, C. L. (2021) · Diagnostic Pathology · doi:10.1186/s13000-021-01130-2 · Cited by 32 · see citing works

Digital pathology for primary diagnosis of screen‐detected breast lesions – experimental data, validation and experience from four centres

Williams, B. J., Hanby, A. M., Millican-Slater, R. A., Verghese, E. T., Nijhawan, A., Wilson, I., Besusparis, J., Clark, D., Snead, D. R. J., Rakha, E. A., et al. (2020) · Histopathology · doi:10.1111/his.14079 · Cited by 32 · see citing works

An update on applications of digital pathology: primary diagnosis; telepathology, education and research

Zia, S., Yildiz-Aktas, I. Z., Zia, F., & Parwani, A. V. (2025) · Diagnostic Pathology · doi:10.1186/s13000-025-01610-9 · Cited by 37 · see citing works

Proliferation Tumour Marker Network (PTM-NET) for the identification of tumour region in Ki67 stained breast cancer whole slide images

Joseph, J., Roudier, M. P., Narayanan, P., Augulis, R., Rocher-Ros, V., Pritchard, A., Gerrard, J., Laurinavičius, A., Harrington, E. A., Barrett, J. C., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-49139-4 · Cited by 31 · see citing works

Artificial Intelligence Improves the Accuracy in Histologic Classification of Breast Lesions

Polónia, A., Campelos, S., Ribeiro, A. S., Aymoré, I. L., Pinto, D. G., Biskup-Frużyńska, M., Veiga, R. S., Canas‐Marques, R., Aresta, G., Araújo, T., et al. (2020) · American Journal of Clinical Pathology · doi:10.1093/ajcp/aqaa151 · Cited by 31 · see citing works

Traditional and Modern Diagnostic Approaches in Diagnosing Pediatric Helicobacter pylori Infection

Mărginean, C. O., Meliţ, L. E., & Săsăran, M. O. (2022) · Children · doi:10.3390/children9070994 · Cited by 31 · see citing works

Screening of normal endoscopic large bowel biopsies with interpretable graph learning: a retrospective study

Graham, S., Minhas, F., Bilal, M., Ali, M., Tsang, Y. W., Eastwood, M., Wahab, N., Jahanifar, M., Hero, E., Dodd, K., et al. (2023) · Gut · doi:10.1136/gutjnl-2023-329512 · Cited by 32 · see citing works

Accuracy of Digital Pathologic Analysis vs Traditional Microscopy in the Interpretation of Melanocytic Lesions

Onega, T., Barnhill, R. L., Piepkorn, M. W., Longton, G., Elder, D. E., Weinstock, M. A., Knezevich, S. R., Reisch, L. M., Carney, P. A., Nelson, H. D., et al. (2018) · JAMA Dermatology · doi:10.1001/jamadermatol.2018.2388 · Cited by 30 · see citing works

Diagnostic Quality Model (DQM): an integrated framework for the assessment of diagnostic quality when using AI/ML

Lennerz, J. K., Salgado, R., Kim, G. E., Sirintrapun, S. J., Thierauf, J. C., Singh, A., Indave, I., Bard, A., Weissinger, S. E., Heher, Y. K., et al. (2023) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2022-1151 · Cited by 29 · see citing works

Ensembling Neural Networks for Digital Pathology Images Classification and Segmentation

Pimkin, A., Makarchuk, G., Кондратенко, В., Pisov, M., Krivov, E., & Belyaev, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93000-8_100 · Cited by 28 · see citing works

Diagnostic of Helicobacter pylori infection

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Automatic ganglion cell detection for improving the efficiency and accuracy of hirschprung disease diagnosis

Greenberg, A., Aizic, A., Zubkov, A., Borsekofsky, S., Hagege, R. R., & Hershkovitz, D. (2021) · Scientific Reports · doi:10.1038/s41598-021-82869-y · Cited by 27 · 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

Perineural invasion detection in pancreatic ductal adenocarcinoma using artificial intelligence

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Remote Reporting from Home for Primary Diagnosis in Surgical Pathology: A Tertiary Oncology Center Experience during the COVID-19 Pandemic

Rao, V., Kumar, R., Rajaganesan, S., Rane, S. U., Deshpande, G., Yadav, S. C., Patii, A., Pai, T., Menon, S. G., Shah, A., et al. (2021) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_72_20 · Cited by 26 · see citing works

Verification and Validation of Digital Pathology (Whole Slide Imaging) for Primary Histopathological Diagnosis: All Wales Experience

Babawale, M. O., Gunavardhan, A., Walker, J. D., Corfield, T., Huey, P., Savage, A., Bansal, A., Atkinson, M., Abdelsalam, H., Raweily, E., et al. (2021) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_55_20 · Cited by 32 · see citing works

Digital slide viewing for primary reporting in gastrointestinal pathology: a validation study

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Digital pathology for reporting histopathology samples, including cancer screening samples – definitive evidence from a multisite study

Azam, A. S., Tsang, Y., Thirlwall, J., Kimani, P., Sah, S. P., Gopalakrishnan, K., Boyd, C., Loughrey, M. B., Kelly, P. J., Boyle, D. P., et al. (2024) · Histopathology · doi:10.1111/his.15129 · Cited by 12 · see citing works

Diagnostic concordance and discordance in digital pathology: a systematic review and meta-analysis

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Clinical Examination and Validation of Primary Diagnosis in Anatomic Pathology Using Whole Slide Digital Images

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