Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
Open accessPublished version of a preprint
Abstract
Visual inspection of histopathology slides is one of the main methods used by pathologists to assess the stage, type and subtype of lung tumors. Adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most prevalent subtypes of lung cancer, and their distinction requires visual inspection by an experienced pathologist. In this study, we trained a deep convolutional neural network (inception v3) on whole-slide images obtained from The Cancer Genome Atlas to accurately and automatically classify them into LUAD, LUSC or normal lung tissue. The performance of our method is comparable to that of pathologists, with an average area under the curve (AUC) of 0.97. Our model was validated on independent datasets of frozen tissues, formalin-fixed paraffin-embedded tissues and biopsies. Furthermore, we trained the network to predict the ten most commonly mutated genes in LUAD. We found that six of them-STK11, EGFR, FAT1, SETBP1, KRAS and TP53-can be predicted from pathology images, with AUCs from 0.733 to 0.856 as measured on a held-out population. These findings suggest that deep-learning models can assist pathologists in the detection of cancer subtype or gene mutations. Our approach can be applied to any cancer type, and the code is available at https://github.com/ncoudray/DeepPATH .
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Patch-level approaches ignore up to 99% of the total tissue.
“By design, these patch-level approaches ignore up to 99% of the total tissue, miss potentially diagnostically relevant areas, and cannot be applied to tasks that require the full context of the entire tissue sample (e.g., tumor thickness prediction).”
From Generating dermatopathology reports from gigapixel whole slide images with HistoGPT (2025) · cites this paper as [11]
The Results section (“A deep learning framework for the automatic analysis of histopathology images”; page unavailable) says the study divided whole-slide images into tens to thousands of non-overlapping patches per slide, rather than selecting a small fraction of tissue. The Statistical Analysis methods also describe aggregating predictions from the slide’s tiles. The paper does not report that its patch-level approach ignores up to 99% of tissue; its described approach runs contrary to that characterization. (LLM, conf=0.83)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe lower WSI attribution in BRCA, COADREAD, and LUAD may result from prognostic information also being partly explained by genomics, because aberrations such as ERBB2 or KRAS mutation and microsatellite instability can be determined from histology.
“which may result from prognostic information also partially explained via genomics as aberrations such as ERBB2 or KRAS mutation and the presence of microsatellite instability can be determined from histology (Coudray et al., 2018;Kather et al.,2019,2020;Gamble et al.,2021).”
From Pan-cancer integrative histology-genomic analysis via multimodal deep learning (2022) · cites this paper as Coudray et al., 2018
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredPatch-level approaches cannot be applied to tasks that require the full context of the entire tissue sample.
“By design, these patch-level approaches ignore up to 99% of the total tissue, miss potentially diagnostically relevant areas, and cannot be applied to tasks that require the full context of the entire tissue sample (e.g., tumor thickness prediction).”
From Generating dermatopathology reports from gigapixel whole slide images with HistoGPT (2025) · cites this paper as [11]
The Results section ('A deep learning framework for the automatic analysis of histopathology images'; page not provided) says the network used 512×512-pixel tiles because whole-slide images were too large for direct input. This supports the point that an individual patch lacks whole-sample context, but the paper does not say patch-level approaches cannot be applied to tasks requiring that context, or discuss tumor-thickness prediction. (LLM, conf=0.78)
Experimental — accuracy not yet measuredPatch-level approaches miss potentially diagnostically relevant areas.
“By design, these patch-level approaches ignore up to 99% of the total tissue, miss potentially diagnostically relevant areas, and cannot be applied to tasks that require the full context of the entire tissue sample (e.g., tumor thickness prediction).”
From Generating dermatopathology reports from gigapixel whole slide images with HistoGPT (2025) · cites this paper as [11]
The Results section (‘A deep learning framework’; page unavailable) describes dividing whole-slide images into non-overlapping patches and producing tens to thousands of tiles per slide. The Online Methods (‘Tests on independent cohorts’; page unavailable) describe restricting some tests to pathologist-selected regions, but neither passage says patch-level approaches miss diagnostically relevant areas. I searched for missed diagnostic areas and tissue excluded by patch selection. (LLM, conf=0.74)
Experimental — accuracy not yet measuredStudies have shown that morphological features captured by a hematoxylin and eosin stain are predictive of molecular biomarkers utilized in theragnosis.
“Studies have even shown that morphological features captured by a hematoxylin and eosin (H&E) stain are predictive of molecular biomarkers utilized in theragnosis 85,89 .”
From Deep learning-enabled medical computer vision (2021) · cites this paper as [85]
Convolutional neural networks have demonstrated performance comparable to that of specialists in cancer detection and tumor segmentation.
“Convolutional neural networks (CNNs), built for spatial data, have demonstrated performance comparable to that of specialists in cancer detection and tumor segmentation [15,16,17,18,19,20].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [19]
Coudray et al. predicted the ten most frequent mutations in lung adenocarcinoma by training neural networks, and six mutations (KRAS, STK11, TP53, EGFR, SETBP1, and FAT1) were identifiable from pathology images with accuracies ranging from 73.3% to 85.6%.
“Coudray et al. proposed that specific gene alterations might influence the morphological appearance of cancer cells on histologic slides; by training neural networks, they were able to predict the ten most frequent mutations in lung adenocarcinoma [36].”
From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [36]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · 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. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works
A Coefficient of Agreement for Nominal Scales
Cohen, J. (1960) · Educational and Psychological Measurement · doi:10.1177/001316446002000104 · Cited by 42,398 · see citing works
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500 · see citing works
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138 · see citing works
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
Hanley, J. A., & McNeil, B. J. (1982) · Radiology · doi:10.1148/radiology.143.1.7063747 · Cited by 21,856 · see citing works
Interrater reliability: The kappa statistic
McHugh, M. L. (2012) · Biochemia Medica · doi:10.11613/bm.2012.031 · Cited by 19,877 · see citing works
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839 · see citing works
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697 · see citing works
Comprehensive molecular profiling of lung adenocarcinoma
The Cancer Genome Atlas Research Network (2014) · Nature · doi:10.1038/nature13385 · Cited by 5,732 · see citing works
Deep learning in medical image analysis
Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968 · see citing works
Travis, W. D., Brambilla, E. M., Noguchi, M., Nicholson, A. G., Geisinger, K. R., Yatabe, Y., Beer, D. G., Powell, C. A., Riely, G. J., Schil, P. E. V., et al. (2011) · Journal of Thoracic Oncology · doi:10.1097/jto.0b013e318206a221 · Cited by 4,780 · see citing works
Mutational landscape and significance across 12 major cancer types
Kandoth, C., McLellan, M. D., Vandin, F., Ye, K., Niu, B., Lu, C., Xie, M., Zhang, Q., McMichael, J. F., Wyczalkowski, M. A., et al. (2013) · Nature · doi:10.1038/nature12634 · Cited by 4,419 · see citing works
Comprehensive genomic characterization of squamous cell lung cancers
The Cancer Genome Atlas Research Network (2012) · Nature · doi:10.1038/nature11404 · Cited by 3,994 · see citing works
Genotypic and Histological Evolution of Lung Cancers Acquiring Resistance to EGFR Inhibitors
Sequist, L. V., Waltman, B. A., Dias-Santagata, D., Digumarthy, S., Turke, A. B., Fidias, P., Bergethon, K., Shaw, A. T., Gettinger, S., Cosper, A. K., et al. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3002003 · Cited by 3,499 · see citing works
An Introduction to the Bootstrap.
Buckland, S. T., Efron, B., & Tibshirani, R. J. (1994) · Biometrics · doi:10.2307/2532810 · Cited by 3,236 · see citing works
CNN architectures for large-scale audio classification
Hershey, S., Chaudhuri, S., Ellis, D. P. W., Gemmeke, J. F., Jansen, A., Moore, R. C., Plakal, M., Platt, D., Saurous, R. A., Seybold, B., et al. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952132 · Cited by 2,605 · see citing works
Accelerating t-SNE using tree-based algorithms
van der Maaten, L. (2014) · Journal of machine learning research · doi:10.5555/2627435.2697068 · Cited by 2,207 · see citing works
Greenspan, H. K., van Ginneken, B., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2553401 · Cited by 1,704 · see citing works
Toward a Shared Vision for Cancer Genomic Data
Grossman, R. L., Heath, A. P., Ferretti, V., Varmus, H. E., Lowy, D. R., Kibbe, W. A., & Staudt, L. M. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1607591 · Cited by 1,680 · see citing works
Lung pattern classification for interstitial lung diseases using a deep convolutional neural network
Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A., & Mougiakakou, S. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2535865 · Cited by 1,343 · see citing works
Sirinukunwattana, K., Raza, S. E. A., Tsang, Y.-W., Snead, D. R. J., Cree, I. A., & Rajpoot, N. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2525803 · Cited by 1,224 · see citing works
Perez-Soler, R., Chachoua, A., Hammond, L. A., Rowinsky, E. K., Huberman, M. S., Karp, D. D., Rigas, J. R., Clark, G. M., Santabarbara, P., & Bonomi, P. (2004) · Journal of Clinical Oncology · doi:10.1200/jco.2004.11.057 · Cited by 1,067 · see citing works
Yu, K.-H., Zhang, C., Berry, G. J., Altman, R. B., Ré, C., Rubin, D. L., & Snyder, M. (2016) · Nature Communications · doi:10.1038/ncomms12474 · Cited by 972 · see citing works
Laser Capture Microdissection: Molecular Analysis of Tissue
Bonner, R. F., Emmert‐Buck, M. R., Cole, K. A., Pohida, T. J., Chuaqui, R. F., Goldstein, S. R., & Liotta, L. A. (1997) · Science · doi:10.1126/science.278.5342.1481 · Cited by 877 · see citing works
Masters, G. A., Temin, S., Azzoli, C. G., Giaccone, G., Baker, S., Brahmer, J. R., Ellis, P. M., Gajra, A., Rackear, N., Schiller, J. H., et al. (2015) · Journal of Clinical Oncology · doi:10.1200/jco.2015.62.1342 · Cited by 850 · see citing works
Cheng, J.-Z., Ni, D., Chou, Y.-H., Qin, J., Tiu, C.-M., Chang, Y.-C., Huang, C.-S., Shen, D., & Chen, C.-M. (2016) · Scientific Reports · doi:10.1038/srep24454 · Cited by 755 · see citing works
Targeted therapy for non-small cell lung cancer: current standards and the promise of the future.
(2015) · Europe PMC (PubMed Central) · doi:10.3978/j.issn.2218-6751.2014.05.01 · Cited by 716 · see citing works
Rodig, S. J., Mino-Kenudson, M. A., Đačić, S., Yeap, B. Y., Shaw, A. T., Barletta, J. A., Stubbs, H., Law, K., Lindeman, N. I., Mark, E. J., et al. (2009) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-09-0802 · Cited by 677 · see citing works
Jänne, P. A., Shaw, A. T., Pereira, J. R., Jeannin, G., Vansteenkiste, J., Barrios, C., Franke, F. A., Grinsted, L., Zazulina, V., Smith, P., et al. (2013) · The Lancet Oncology · doi:10.1016/s1470-2045(12)70489-8 · Cited by 655 · see citing works
Dearden, S., Stevens, J., Wu, Y.-L., & Blowers, D. (2013) · Annals of Oncology · doi:10.1093/annonc/mdt205 · Cited by 646 · see citing works
Hanna, N., Johnson, D., Temin, S., Baker, S., Brahmer, J., Ellis, P. M., Giaccone, G., Hesketh, P. J., Jaiyesimi, I., Leighl, N. B., et al. (2017) · Journal of Clinical Oncology · doi:10.1200/jco.2017.74.6065 · Cited by 643 · see citing works
Inactivation of LKB1/STK11 is a common event in adenocarcinomas of the lung.
Sánchez‐Céspedes, M., Parrella, P., Esteller, M., Nomoto, S., Trink, B., Engles, J. M., Westra, W. H., Herman, J. G., & Sidransky, D. (2002) · PubMed · Cited by 626 · see citing works
Shackelford, D. B., Abt, E., Gerken, L., Vasquez, D. S., Seki, A., Leblanc, M., Wei, L., Fishbein, M. C., Czernin, J., Mischel, P. S., et al. (2013) · Cancer Cell · doi:10.1016/j.ccr.2012.12.008 · Cited by 585 · see citing works
Quantification of Free Circulating DNA As a Diagnostic Marker in Lung Cancer
Sozzi, G., Conte, D., Leon, M., Cirincione, R., Roz, L., Ratcliffe, C., Roz, E., Cirenei, N., Bellomi, M., Pelosi, G., et al. (2003) · Journal of Clinical Oncology · doi:10.1200/jco.2003.02.006 · Cited by 548 · see citing works
Cruz-Roa, Á., Gilmore, H. L., Basavanhally, A. N., Feldman, M. D., Ganesan, S., Shih, N. N. C., Tomaszewski, J., Gonzalez, F. A., & Madabhushi, A. (2017) · Scientific Reports · doi:10.1038/srep46450 · Cited by 530 · see citing works
TP53 Mutations in Nonsmall Cell Lung Cancer
Mogi, A., & Kuwano, H. (2011) · BioMed Research International · doi:10.1155/2011/583929 · Cited by 392 · see citing works
Recurrent somatic mutation of FAT1 in multiple human cancers leads to aberrant Wnt activation
Morris, L. G. T., Kaufman, A., Gong, Y., Ramaswami, D., Walsh, L. A., Turcan, Ş., Eng, S., Kannan, K. S., Zou, Y., Peng, L., et al. (2013) · Nature Genetics · doi:10.1038/ng.2538 · Cited by 366 · see citing works
An Automatic Learning-Based Framework for Robust Nucleus Segmentation
Xing, F., Xie, Y., & Yang, L. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2481436 · Cited by 353 · see citing works
Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images
Khosravi, P., Kazemi, E., Imielinski, M., Elemento, O., & Hajirasouliha, I. (2018) · EBioMedicine · doi:10.1016/j.ebiom.2017.12.026 · Cited by 334 · see citing works
Comprehensive Histologic Analysis of ALK-Rearranged Lung Carcinomas
Yoshida, A., Tsuta, K., Nakamura, H., Kohno, T., Takahashi, F., Asamura, H., Sekine, I., Fukayama, M., Shibata, T., Furuta, K., et al. (2011) · The American Journal of Surgical Pathology · doi:10.1097/pas.0b013e3182233e06 · Cited by 281 · see citing works
Ertosun, M. G., & Rubin, D. L. (2015) · PubMed · Cited by 255 · see citing works
EGFR Mutation Is Specific for Terminal Respiratory Unit Type Adenocarcinoma
Yatabe, Y., Kosaka, T., Takahashi, T., & Mitsudomi, T. (2005) · The American Journal of Surgical Pathology · doi:10.1097/01.pas.0000157935.28066.35 · Cited by 238 · see citing works
Mutation-Specific Antibodies for the Detection of EGFR Mutations in Non–Small-Cell Lung Cancer
Yu, J. Q., Kane, S. E., Wu, J., Benedettini, E., Li, D., Reeves, C., Innocenti, G., Wetzel, R., Crosby, K., Becker, A., et al. (2009) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-08-2739 · Cited by 235 · see citing works
Terry, J., Leung, S. C. Y., Laskin, J. J., et al. (2010) · The American Journal of Surgical Pathology · doi:10.1097/pas.0b013e3181f7dae3 · Cited by 198 · see citing works
Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis
Luo, X., Zang, X., Yang, L., Huang, J., Liang, F., Rodriguez‐Canales, J., Wistuba, I. I., Gazdar, A. F., Xie, Y., & Xiao, G. (2016) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2016.10.017 · Cited by 189 · see citing works
IDH2 Mutations Define a Unique Subtype of Breast Cancer with Altered Nuclear Polarity
Chiang, S., Weigelt, B., Wen, H., Pareja, F. G., Raghavendra, A., Martelotto, L. G., Burke, K. A., Basili, T., Li, A., Geyer, F. C., et al. (2016) · Cancer Research · doi:10.1158/0008-5472.can-16-0298 · Cited by 136 · see citing works
Self-citationLKB1 tumor suppressor protein: PARtaker in cell polarity
Baas, A. F., Smit, L., & Clevers, H. C. (2004) · Trends in Cell Biology · doi:10.1016/j.tcb.2004.04.001 · Cited by 131 · see citing works
Warth, A., Penzel, R., Lindenmaier, H., Brandt, R., Stenzinger, A., Herpel, E., Goeppert, B. F., Thomas, M., Herth, F. J. F., Dienemann, H., et al. (2013) · European Respiratory Journal · doi:10.1183/09031936.00018013 · Cited by 111 · see citing works
Current State of the Regulatory Trajectory for Whole Slide Imaging Devices in the USA
Abels, E., & Pantanowitz, L. (2017) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_11_17 · Cited by 106 · see citing works
Correlation between morphology and EGFR mutations in lung adenocarcinomas
Ninomiya, H., Hiramatsu, M., Inamura, K., Nomura, K., Okui, M., Miyoshi, T., Okumura, S., Satoh, Y., Nakagawa, K., Nishio, M., et al. (2008) · Lung Cancer · doi:10.1016/j.lungcan.2008.04.017 · Cited by 99 · see citing works
Simon, O., Yacoub, R., Jain, S., Tomaszewski, J. E., & Sarder, P. (2018) · Scientific Reports · doi:10.1038/s41598-018-20453-7 · Cited by 91 · see citing works
Prognostic and predictive biomarkers in lung cancer. A review
Thunnissen, E., der Oord, K. V., & den Bakker, M. (2014) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-014-1535-4 · Cited by 89 · see citing works
Molecular characterization of pulmonary sarcomatoid carcinoma: analysis of 33 cases
Terra, S. B. S. P., Jang, J. S., Bi, L., Kipp, B. R., Jen, J., Yi, E. S., & Boland, J. M. (2016) · Modern Pathology · doi:10.1038/modpathol.2016.89 · Cited by 85 · 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
Oncology Drug Approvals: Evaluating Endpoints and Evidence in an Era of Breakthrough Therapies
Blumenthal, G. M., Kluetz, P. G., Schneider, J., et al. (2017) · The Oncologist · doi:10.1634/theoncologist.2017-0152 · Cited by 71 · see citing works
The current status of targeted therapy for non‐small cell lung cancer
Francis, H., & Solomon, B. (2009) · Internal Medicine Journal · doi:10.1111/j.1445-5994.2009.02141.x · Cited by 66 · see citing works
Role of LKB1 in lung cancer development
Makowski, L., & Hayes, D. N. (2008) · British Journal of Cancer · doi:10.1038/sj.bjc.6604515 · Cited by 57 · see citing works
Automatic segmentation of histopathological slides of renal tissue using deep learning
de Bel, T., Hermsen, M., van der Laak, J. A. W. M., Litjens, G. J. S., Smeets, B., & Hilbrands, L. B. (2018) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2293717 · Cited by 55 · see citing works
Mishra, R., Daescu, O., Leavey, P. J., Rakheja, D., & Sengupta, A. L. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59575-7_2 · Cited by 44 · see citing works
Current status of targeted therapy in non small cell lung cancer (NSCLC)
Parums, D. V. (2014) · Drugs of today · doi:10.1358/dot.2014.50.07.2185913 · Cited by 40 · see citing works
Changes in p53 expression can modify cell shape of ras-transformed fibroblasts and epitheliocytes
Gloushankova, N. A., Ossovskaya, V., Vasiliev, J. M., et al. (1997) · Oncogene · doi:10.1038/sj.onc.1201483 · Cited by 35 · see citing works
Current status of targeted therapy in non small cell lung cancer (NSCLC)
Parums, D. (2014) · Drugs of today · doi:10.1358/dot.2014.50.7.2185913 · Cited by 34 · see citing works
Zachara-Szczakowski, S., Verdun, T., & Churg, A. (2015) · Human Pathology · doi:10.1016/j.humpath.2015.02.001 · Cited by 24 · see citing works
Tumor Segmentation in Whole Slide Images Using Persistent Homology and Deep Convolutional Features
Qaiser, T., Tsang, Y., Epstein, D. B. A., & Rajpoot, N. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-60964-5_28 · Cited by 24 · see citing works
Dimou, A., Agarwal, S., Anagnostou, V., et al. (2011) · American Journal Of Pathology · doi:10.1016/j.ajpath.2011.04.031 · Cited by 23 · see citing works
Automated segmentation of epithelial tissue in prostatectomy slides using deep learning
Bulten, W., Litjens, G. J. S., de Kaa, C. A. H., et al. (2018) · SPIE Medical Imaging: Digital Pathology · doi:10.1117/12.2292872 · Cited by 19 · see citing works
Donovan, M. J., Kotsianti, A., Bayer‐Zubek, V., Verbel, D., Teverovskiy, M., Cordon‐Cardo, C., Costa, J. C., Greco, F. A., Hainsworth, J. D., & Parums, D. V. (2009) · European Journal of Cancer · doi:10.1016/j.ejca.2009.02.004 · Cited by 18 · see citing works
Houang, M., Sioson, L., Clarkson, A., Watson, N., Farzin, M., Toon, C. W., Raut, A., O'Toole, S. A., Cooper, W. A., Pavlakis, N., et al. (2014) · Pathology · doi:10.1097/pat.0000000000000141 · Cited by 12 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- [18]Qaiser T, Tsang Y-W, Epstein D & RajpootEma N in Medical Image Understanding and Analysis: 21st Annual Conference on Medical Image Understanding and Analysis. (ed Springer International Publishing).
- [21]de Bel T in Medical Imaging 2018: Digital Pathology Vol. 10581 (ed International Society for Optics and Photonics.) 1058112 (2018).
- [26]Ertosun MG & Rubin DL in AMIA Annual Symposium Proceedings. (ed American Medical Informatics Association).
- [28]Mishra R, Daescu O, Leavey P, Rakheja D & Sengupta A in International Symposium on Bioinformatics Research and Applications. (ed Springer) 12– 23.
- [42]Zeiler MD & Fergus R in European Conference on Computer Vision. 818– 833.
Cited by
Sorted by citations
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450 · see citing works
Applications of machine learning in drug discovery and development
Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., et al. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0024-5 · Cited by 3,228 · see citing works
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K. J., Brogi, E., Reuter, V. E., Klimstra, D. S., & Fuchs, T. J. (2019) · Nature Medicine · doi:10.1038/s41591-019-0508-1 · Cited by 2,799 · see citing works
Data-efficient and weakly supervised computational pathology on whole-slide images
Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Chen, R. J., Barbieri, M., & Mahmood, F. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-020-00682-w · Cited by 2,282 · see citing works
Liu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., Bruynseels, A., Mahendiran, T., Moraes, G., Shamdas, M., Kern, C., et al. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30123-2 · Cited by 1,883 · see citing works
Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V., & Madabhushi, A. (2019) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-019-0252-y · Cited by 1,698 · see citing works
Kather, J. N., Pearson, A. T., Halama, N., Jäger, D., Krause, J., Loosen, S. H., Marx, A., Boor, P., Tacke, F., Neumann, U. P., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0462-y · Cited by 1,458 · see citing works
Towards a general-purpose foundation model for computational pathology
Chen, R. J., Ding, T., Lu, M. Y., Williamson, D. F. K., Jaume, G., Song, A. H., Chen, B., Zhang, A., Shao, D., Shaban, M., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02857-3 · Cited by 1,560 · see citing works
Deep learning-enabled medical computer vision
Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., Liu, Y., Topol, E., Dean, J., & Socher, R. (2021) · npj Digital Medicine · doi:10.1038/s41746-020-00376-2 · Cited by 1,406 · see citing works
Deep learning for cellular image analysis
Moen, E., Bannon, D., Kudo, T., Graf, W., Covert, M., & Van Valen, D. (2019) · Nature Methods · doi:10.1038/s41592-019-0403-1 · Cited by 1,225 · see citing works
Digital pathology and artificial intelligence
Niazi, M. K. K., Parwani, A. V., & Gurcan, M. N. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30154-8 · Cited by 1,232 · see citing works
Video-based AI for beat-to-beat assessment of cardiac function
Ouyang, D., He, B., Ghorbani, A., Yuan, N., Ebinger, J., Langlotz, C. P., Heidenreich, P. A., Harrington, R. A., Liang, D. H., Ashley, E. A., et al. (2020) · Nature · doi:10.1038/s41586-020-2145-8 · Cited by 1,153 · see citing works
A visual-language foundation model for computational pathology
Lu, M. Y., Chen, B., Williamson, D. F. K., Chen, R. J., Liang, I., Ding, T., Jaume, G., Odintsov, I., Le, L. P., Gerber, G., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02856-4 · Cited by 966 · see citing works
Deep learning in histopathology: the path to the clinic
van der Laak, J., Litjens, G., & Ciompi, F. (2021) · Nature Medicine · doi:10.1038/s41591-021-01343-4 · Cited by 902 · see citing works
Rasmy, L., Xiang, Y., Xie, Z., Tao, C., & Zhi, D. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00455-y · Cited by 886 · see citing works
A whole-slide foundation model for digital pathology from real-world data
Xu, H., Usuyama, N., Bagga, J., Zhang, S., Rao, R., Naumann, T., Wong, C., Gero, Z., González, J., Gu, Y., et al. (2024) · Nature · doi:10.1038/s41586-024-07441-w · Cited by 921 · see citing works
Hollon, T. C., Pandian, B., Adapa, A. R., Urias, E., Save, A. V., Khalsa, S. S. S., Eichberg, D. G., D’Amico, R. S., Farooq, Z. U., Lewis, S., et al. (2020) · Nature Medicine · doi:10.1038/s41591-019-0715-9 · Cited by 790 · see citing works
Artificial Intelligence in Cancer Research and Precision Medicine
Bhinder, B., Gilvary, C., Madhukar, N. S., & Elemento, O. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-0090 · Cited by 781 · see citing works
Artificial intelligence for multimodal data integration in oncology
Lipkova, J., Chen, R. J., Chen, B., Lu, M. Y., Barbieri, M., Shao, D., Vaidya, A. J., Chen, C., Zhuang, L., Williamson, D. F. K., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.09.012 · Cited by 777 · see citing works
Deep neural network models for computational histopathology: A survey
Srinidhi, C. L., Ciga, O., & Martel, A. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101813 · Cited by 749 · see citing works
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
Deep learning in cancer pathology: a new generation of clinical biomarkers
Echle, A., Rindtorff, N. T., Brinker, T. J., Luedde, T., Pearson, A. T., & Kather, J. N. (2021) · British Journal of Cancer · doi:10.1038/s41416-020-01122-x · Cited by 667 · see citing works
Harnessing multimodal data integration to advance precision oncology
Boehm, K. M., Khosravi, P., Vanguri, R., Gao, J., & Shah, S. P. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-021-00408-3 · Cited by 662 · see citing works
Pan-cancer integrative histology-genomic analysis via multimodal deep learning
Chen, R. J., Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Lipkova, J., Noor, Z., Shaban, M., Shady, M., Williams, M., Joo, B., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.07.004 · Cited by 660 · see citing works
Chen, R. J., Lu, M. Y., Wang, J., Williamson, D. F. K., Rodig, S. J., Lindeman, N. I., & Mahmood, F. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3021387 · Cited by 729 · see citing works
AI-based pathology predicts origins for cancers of unknown primary
Lu, M. Y., Chen, T. Y., Williamson, D. F. K., Zhao, M., Shady, M., Lipkova, J., & Mahmood, F. (2021) · Nature · doi:10.1038/s41586-021-03512-4 · Cited by 636 · see citing works
Deep learning for prediction of colorectal cancer outcome: a discovery and validation study
Skrede, O.-J., De Raedt, S., Kleppe, A., Hveem, T. S., Liestøl, K., Maddison, J., Askautrud, H. A., Pradhan, M., Nesheim, J. A., Albregtsen, F., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(19)32998-8 · Cited by 627 · see citing works
Digital pathology and artificial intelligence in translational medicine and clinical practice
Baxi, V., Edwards, R., Montalto, M., & Saha, S. (2022) · Modern Pathology · doi:10.1038/s41379-021-00919-2 · Cited by 592 · see citing works
A foundation model for clinical-grade computational pathology and rare cancers detection
Vorontsov, E., Bozkurt, A., Casson, A., Shaikovski, G., Zelechowski, M., Severson, K., Zimmermann, E., Hall, J., Tenenholtz, N., Fusi, N., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03141-0 · Cited by 572 · see citing works
A pathology foundation model for cancer diagnosis and prognosis prediction
Wang, X., Zhao, J., Marostica, E., Yuan, W., Jin, J., Zhang, J., Li, R., Tang, H., Wang, K., Li, Y., et al. (2024) · Nature · doi:10.1038/s41586-024-07894-z · Cited by 580 · see citing works
Deep learning-based classification of mesothelioma improves prediction of patient outcome
Courtiol, P., Maussion, C., Moarii, M., Pronier, E., Pilcer, S., Sefta, M., Manceron, P., Toldo, S., Zaslavskiy, M., Le Stang, N., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0583-3 · Cited by 544 · see citing works
A deep learning model to predict RNA-Seq expression of tumours from whole-slide images
Schmauch, B., Romagnoni, A., Pronier, E., Saillard, C., Maillé, P., Calderaro, J., Kamoun, A., Sefta, M., Toldo, S., Zaslavskiy, M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17678-4 · Cited by 533 · see citing works
Enabling Technologies for Personalized and Precision Medicine
Ho, D., Quake, S. R., McCabe, E. R. B., Chng, W. J., Chow, E. K., Ding, X., Gelb, B. D., Ginsburg, G. S., Hassenstab, J., Ho, C.-M., et al. (2020) · Trends in Biotechnology · doi:10.1016/j.tibtech.2019.12.021 · Cited by 517 · see citing works
Integrating spatial gene expression and breast tumour morphology via deep learning
He, B., Bergenstråhle, L., Stenbeck, L., Abid, A., Andersson, A., Borg, Å., Maaskola, J., Lundeberg, J., & Zou, J. (2020) · Nature Biomedical Engineering · doi:10.1038/s41551-020-0578-x · Cited by 507 · see citing works
Deep learning interpretation of echocardiograms
Ghorbani, A., Ouyang, D., Abid, A., He, B., Chen, J. H., Harrington, R. A., Liang, D. H., Ashley, E. A., & Zou, J. Y. (2020) · npj Digital Medicine · doi:10.1038/s41746-019-0216-8 · Cited by 518 · see citing works
Qiu, S., Joshi, P. S., Miller, M. I., Xue, C., Zhou, X., Karjadi, C., Chang, G. H., Joshi, A. S., Dwyer, B., Zhu, S., et al. (2020) · Brain · doi:10.1093/brain/awaa137 · Cited by 472 · see citing works
A multimodal generative AI copilot for human pathology
Lu, M. Y., Chen, B., Williamson, D. F. K., Chen, R. J., Zhao, M., Chow, A. K., Ikemura, K., Kim, A., Pouli, D., Patel, A., et al. (2024) · Nature · doi:10.1038/s41586-024-07618-3 · Cited by 461 · see citing works
Artificial intelligence in histopathology: enhancing cancer research and clinical oncology
Shmatko, A., Ghaffari Laleh, N., Gerstung, M., & Kather, J. N. (2022) · Nature Cancer · doi:10.1038/s43018-022-00436-4 · Cited by 446 · see citing works
Yamashita, R., Long, J., Longacre, T., Peng, L., Berry, G., Martin, B., Higgins, J., Rubin, D. L., & Shen, J. (2021) · The Lancet Oncology · doi:10.1016/s1470-2045(20)30535-0 · Cited by 391 · see citing works
Precision medicine in the era of artificial intelligence: implications in chronic disease management
Subramanian, M., Wojtusciszyn, A., Favre, L., Boughorbel, S., Shan, J., Letaief, K. B., Pitteloud, N., & Chouchane, L. (2020) · Journal of Translational Medicine · doi:10.1186/s12967-020-02658-5 · Cited by 362 · see citing works
Chen, P.-H. C., Gadepalli, K., MacDonald, R., Liu, Y., Kadowaki, S., Nagpal, K., Kohlberger, T., Dean, J., Corrado, G. S., Hipp, J. D., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0539-7 · Cited by 342 · see citing works
Feng, L., Liu, Z., Li, C., Li, Z., Lou, X., Shao, L., Wang, Y., Huang, Y., Chen, H., Pang, X., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00215-6 · Cited by 339 · see citing works
Impact of a deep learning assistant on the histopathologic classification of liver cancer
Kiani, A., Uyumazturk, B., Rajpurkar, P., Wang, A., Gao, R., Jones, E., Yu, Y., Langlotz, C. P., Ball, R. L., Montine, T. J., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0232-8 · Cited by 325 · see citing works
The impact of site-specific digital histology signatures on deep learning model accuracy and bias
Howard, F. M., Dolezal, J., Kochanny, S., Schulte, J., Chen, H., Heij, L., Huo, D., Nanda, R., Olopade, O. I., Kather, J. N., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24698-1 · Cited by 330 · see citing works
Park, S., Ock, C.-Y., Kim, H., Pereira, S., Park, S., Ma, M., Choi, S., Kim, S., Shin, S., Aum, B. J., et al. (2022) · Journal of Clinical Oncology · doi:10.1200/jco.21.02010 · Cited by 280 · see citing works
Sirinukunwattana, K., Domingo, E., Richman, S. D., Redmond, K. L., Blake, A., Verrill, C., Leedham, S. J., Chatzipli, A., Hardy, C., Whalley, C. M., et al. (2021) · Gut · doi:10.1136/gutjnl-2019-319866 · Cited by 264 · see citing works
McGenity, C., Clarke, E. L., Jennings, C., Matthews, G., Cartlidge, C., Freduah-Agyemang, H., Stocken, D. D., & Treanor, D. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01106-8 · Cited by 290 · see citing works
Wagner, S. J., Reisenbüchler, D., West, N. P., Niehues, J. M., Zhu, J., Foersch, S., Veldhuizen, G. P., Quirke, P., Grabsch, H. I., van den Brandt, P. A., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.08.002 · Cited by 275 · see citing works
Translational AI and deep learning in diagnostic pathology
Serag, A., Ion-Margineanu, A., Qureshi, H., McMillan, R., Saint Martin, M.-J., Diamond, J., O'Reilly, P., & Hamilton, P. (2019) · Frontiers in Medicine · doi:10.3389/fmed.2019.00185 · Cited by 274 · see citing works
Federated learning for computational pathology on gigapixel whole slide images
Lu, M. Y., Chen, R. J., Kong, D., Lipkova, J., Singh, R., Williamson, D. F. K., Chen, T. Y., & Mahmood, F. (2022) · Medical Image Analysis · doi:10.1016/j.media.2021.102298 · Cited by 267 · see citing works
Application of Artificial Intelligence in Medicine: An Overview
Liu, P.-R., Lu, L., Zhang, J.-Y., Huo, T.-T., Liu, S.-X., & Ye, Z.-W. (2021) · Current Medical Science · doi:10.1007/s11596-021-2474-3 · Cited by 258 · see citing works
Big data in basic and translational cancer research
Jiang, P., Sinha, S., Aldape, K., Hannenhalli, S., Sahinalp, C., & Ruppin, E. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-022-00502-0 · Cited by 254 · see citing works
A Graph-Transformer for Whole Slide Image Classification
Zheng, Y., Gindra, R. H., Green, E. J., Burks, E. J., Betke, M., Beane, J. E., & Kolachalama, V. B. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3176598 · Cited by 255 · see citing works
Federated learning improves site performance in multicenter deep learning without data sharing
Sarma, K. V., Harmon, S., Sanford, T., Roth, H. R., Xu, Z., Tetreault, J., Xu, D., Flores, M. G., Raman, A. G., Kulkarni, R., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa341 · Cited by 242 · see citing works
Swarm learning for decentralized artificial intelligence in cancer histopathology
Saldanha, O. L., Quirke, P., West, N. P., James, J. A., Loughrey, M. B., Grabsch, H. I., Salto-Tellez, M., Alwers, E., Cifci, D., Ghaffari Laleh, N., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01768-5 · Cited by 227 · see citing works
Cancer proteogenomics: current impact and future prospects
Mani, D. R., Krug, K., Zhang, B., Satpathy, S., Clauser, K. R., Ding, L., Ellis, M., Gillette, M. A., & Carr, S. A. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-022-00446-5 · Cited by 229 · see citing works
The state of the art of deep learning models in medical science and their challenges
Bhatt, C., Kumar, I., Vijayakumar, V., Singh, K. U., & Kumar, A. (2021) · Multimedia Systems · doi:10.1007/s00530-020-00694-1 · Cited by 222 · see citing works
Improved breast cancer histological grading using deep learning
Wang, Y., Acs, B., Robertson, S., Liu, B., Solorzano, L., Wählby, C., Hartman, J., & Rantalainen, M. (2022) · Annals of Oncology · doi:10.1016/j.annonc.2021.09.007 · Cited by 211 · see citing works
CODA: quantitative 3D reconstruction of large tissues at cellular resolution
Kiemen, A. L., Braxton, A. M., Grahn, M. P., Han, K. S., Babu, J. M., Reichel, R., Jiang, A. C., Kim, B., Hsu, J., Amoa, F., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01650-9 · Cited by 218 · see citing works
Noorbakhsh, J., Farahmand, S., Foroughi pour, A., Namburi, S., Caruana, D., Rimm, D., Soltanieh-ha, M., Zarringhalam, K., & Chuang, J. H. (2020) · Nature Communications · doi:10.1038/s41467-020-20030-5 · Cited by 206 · see citing works
Hamamoto, R., Suvarna, K., Yamada, M., Kobayashi, K., Shinkai, N., Miyake, M., Takahashi, M., Jinnai, S., Shimoyama, R., Sakai, A., et al. (2020) · Cancers · doi:10.3390/cancers12123532 · Cited by 207 · see citing works
CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images
Zhou, Y., Graham, S., Alemi Koohbanani, N., Shaban, M., Heng, P.-A., & Rajpoot, N. (2019) · 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2019.00050 · Cited by 225 · see citing works
Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions
Lotter, W., Hassett, M. J., Schultz, N., Kehl, K. L., Van Allen, E. M., & Cerami, E. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-1199 · Cited by 196 · see citing works
Looking beyond the hype: applied AI and machine learning in translational medicine
Toh, T. S., Dondelinger, F., & Wang, D. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.08.027 · Cited by 159 · see citing works
Morphological and molecular breast cancer profiling through explainable machine learning
Binder, A., Bockmayr, M., Hägele, M., Wienert, S., Heim, D., Hellweg, K., Ishii, M., Stenzinger, A., Hocke, A., Denkert, C., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00303-4 · Cited by 156 · see citing works
Artificial Intelligence in Nephrology: Core Concepts, Clinical Applications, and Perspectives
Niel, O., & Bastard, P. (2019) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2019.05.020 · Cited by 165 · see citing works
Levy-Jurgenson, A., Tekpli, X., Kristensen, V. N., & Yakhini, Z. (2020) · Scientific Reports · doi:10.1038/s41598-020-75708-z · Cited by 155 · see citing works
Determining breast cancer biomarker status and associated morphological features using deep learning
Gamble, P., Jaroensri, R., Wang, H., Tan, F., Moran, M., Brown, T., Flament-Auvigne, I., Rakha, E. A., Toss, M., Dabbs, D. J., et al. (2021) · Communications Medicine · doi:10.1038/s43856-021-00013-3 · Cited by 154 · see citing works
Hoang, D.-T., Dinstag, G., Shulman, E. D., Hermida, L. C., Ben-Zvi, D. S., Elis, E., Caley, K., Sammut, S.-J., Sinha, S., Sinha, N., et al. (2024) · Nature Cancer · doi:10.1038/s43018-024-00793-2 · Cited by 157 · see citing works
Quality control stress test for deep learning-based diagnostic model in digital pathology
Schömig-Markiefka, B., Pryalukhin, A., Hulla, W., Bychkov, A., Fukuoka, J., Madabhushi, A., Achter, V., Nieroda, L., Büttner, R., Quaas, A., et al. (2021) · Modern Pathology · doi:10.1038/s41379-021-00859-x · Cited by 146 · see citing works
A multimodal whole-slide foundation model for pathology
Ding, T., Wagner, S. J., Song, A. H., Chen, R. J., Lu, M. Y., Zhang, A., Vaidya, A. J., Jaume, G., Shaban, M., Kim, A., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03982-3 · Cited by 171 · see citing works
Dolezal, J. M., Srisuwananukorn, A., Karpeyev, D., Ramesh, S., Kochanny, S., Cody, B., Mansfield, A. S., Rakshit, S., Bansal, R., Bois, M. C., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34025-x · Cited by 147 · see citing works
Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images
Yu, G., Sun, K., Xu, C., Shi, X.-H., Wu, C., Xie, T., Meng, R.-Q., Meng, X.-H., Wang, K.-S., Xiao, H.-M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26643-8 · Cited by 147 · see citing works
Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling
Filiot, A., Ghermi, R., Olivier, A., Jacob, P., Fidon, L., Camara, A., Mac Kain, A., Saillard, C., & Schiratti, J.-B. (2023) · doi:10.1101/2023.07.21.23292757 · Cited by 148 · see citing works
Rakaee, M., Adib, E., Ricciuti, B., Sholl, L. M., Shi, W., Alessi, J. V., Cortellini, A., Fulgenzi, C. A. M., Viola, P., Pinato, D. J., et al. (2023) · JAMA Oncology · doi:10.1001/jamaoncol.2022.4933 · Cited by 141 · see citing works
Yottixel – An Image Search Engine for Large Archives of Histopathology Whole Slide Images
Kalra, S., Tizhoosh, H. R., Choi, C., Shah, S., Diamandis, P., Campbell, C. J. V., & Pantanowitz, L. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101757 · Cited by 139 · see citing works
Application of Artificial Intelligence in Lung Cancer
Chiu, H.-Y., Chao, H.-S., & Chen, Y.-M. (2022) · Cancers · doi:10.3390/cancers14061370 · Cited by 138 · see citing works
Sha, L., Osinski, B. L., Ho, I. Y., Tan, T. L., Willis, C., Weiss, H., Beaubier, N., Mahon, B. M., Taxter, T. J., & Yip, S. S. F. (2019) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_24_19 · Cited by 130 · 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
A clinical benchmark of public self-supervised pathology foundation models
Campanella, G., Chen, S., Singh, M., Verma, R., Muehlstedt, S., Zeng, J., Stock, A., Croken, M., Veremis, B., Elmas, A., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58796-1 · Cited by 126 · see citing works
Maassen, O., Fritsch, S., Palm, J., Deffge, S., Kunze, J., Marx, G., Riedel, M., Schuppert, A., & Bickenbach, J. (2021) · Journal of Medical Internet Research · doi:10.2196/26646 · Cited by 125 · see citing works
Jiang, Y., Zhou, K., Sun, Z., Wang, H., Xie, J., Zhang, T., Sang, S., Islam, M. T., Wang, J.-Y., Chen, C., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101146 · Cited by 120 · see citing works
Li, F., Yang, Y., Wei, Y., He, P., Chen, J., Zheng, Z., & Bu, H. (2021) · Journal of Translational Medicine · doi:10.1186/s12967-021-03020-z · Cited by 113 · see citing works
Modeling adoption of intelligent agents in medical imaging
Calisto, F. M., Nunes, N., & Nascimento, J. C. (2022) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2022.102922 · Cited by 111 · see citing works
Regression-based Deep-Learning predicts molecular biomarkers from pathology slides
El Nahhas, O. S. M., Loeffler, C. M. L., Carrero, Z. I., van Treeck, M., Kolbinger, F. R., Hewitt, K. J., Muti, H. S., Graziani, M., Zeng, Q., Calderaro, J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45589-1 · Cited by 108 · see citing works
Malik, H., Farooq, M. S., Khelifi, A., Abid, A., Nasir Qureshi, J., & Hussain, M. (2020) · IEEE Access · doi:10.1109/access.2020.3004766 · Cited by 104 · see citing works
Deep learned tissue “fingerprints” classify breast cancers by ER/PR/Her2 status from H&E images
Rawat, R. R., Ortega, I., Roy, P., Sha, F., Shibata, D., Ruderman, D., & Agus, D. B. (2020) · Scientific Reports · doi:10.1038/s41598-020-64156-4 · Cited by 100 · see citing works
Representation Learning of Histopathology Images using Graph Neural Networks
Adnan, M., Kalra, S., & Tizhoosh, H. R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00502 · Cited by 109 · see citing works
Liao, H., Long, Y., Han, R., Wang, W., Xu, L., Liao, M., Zhang, Z., Wu, Z., Shang, X., Li, X., et al. (2020) · Clinical and Translational Medicine · doi:10.1002/ctm2.102 · Cited by 92 · see citing works
Artificial intelligence in food science and nutrition: A narrative review
Miyazawa, T., Hiratsuka, Y., Toda, M., Hatakeyama, N., Ozawa, H., Abe, C., Cheng, T.-Y., Matsushima, Y., Miyawaki, Y., Ashida, K., et al. (2022) · Nutrition Reviews · doi:10.1093/nutrit/nuac033 · Cited by 91 · see citing works
Shao, L., Liu, Z., Feng, L., Lou, X., Li, Z., Zhang, X.-Y., Wan, X., Zhou, X., Sun, K., Zhang, D.-F., et al. (2020) · Annals of Surgical Oncology · doi:10.1245/s10434-020-08659-4 · Cited by 92 · see citing works
Ozyoruk, K. B., Can, S., Darbaz, B., Başak, K., Demir, D., Gokceler, G. I., Serin, G., Hacisalihoglu, U. P., Kurtuluş, E., Lu, M. Y., et al. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00952-9 · Cited by 86 · see citing works
FairLens: Auditing black-box clinical decision support systems
Panigutti, C., Perotti, A., Panisson, A., Bajardi, P., & Pedreschi, D. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102657 · Cited by 86 · see citing works
Overcoming the challenges to implementation of artificial intelligence in pathology
Reis-Filho, J. S., & Kather, J. N. (2023) · JNCI: Journal of the National Cancer Institute · doi:10.1093/jnci/djad048 · Cited by 79 · see citing works
Puyol-Antón, E., Ruijsink, B., Mariscal Harana, J., Piechnik, S. K., Neubauer, S., Petersen, S. E., Razavi, R., Chowienczyk, P., & King, A. P. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.859310 · Cited by 85 · see citing works
Machine learning methods for histopathological image analysis: updates in 2024
Komura, D., Ochi, M., & Ishikawa, S. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.12.033 · Cited by 76 · see citing works
Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology
Song, A. H., Chen, R. J., Ding, T., Williamson, D. F. K., Jaume, G., & Mahmood, F. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01099 · Cited by 79 · see citing works
Real-world deployment of a fine-tuned pathology foundation model for lung cancer biomarker detection
Campanella, G., Kumar, N., Nanda, S., Singi, S., Fluder, E., Kwan, R., Muehlstedt, S., Pfarr, N., Schüffler, P. J., Häggström, I., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03780-x · Cited by 69 · see citing works
Benchmarking foundation models as feature extractors for weakly supervised computational pathology
Neidlinger, P., El Nahhas, O. S. M., Muti, H. S., Lenz, T., Hoffmeister, M., Brenner, H., van Treeck, M., Langer, R., Dislich, B., Behrens, H. M., et al. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01516-3 · Cited by 80 · see citing works
Turner, O. C., Aeffner, F., Bangari, D. S., High, W., Knight, B., Forest, T., Cossic, B., Himmel, L. E., Rudmann, D. G., Bawa, B., et al. (2020) · Toxicologic Pathology · doi:10.1177/0192623319881401 · Cited by 64 · see citing works
- Authors
- Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A.
- Affiliations
- New York University (US)
- National Technical University of Athens (GR)
- NYU Langone Health (US)
- Corresponding author
- Razavian, Narges
- Tsirigos, Aristotelis
- ORCID
- Venue
- Nature Medicine
- Published
- 2018-09-17
- Volume
- 24
- Issue
- 10
- Pages
- 1559-1567
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC9847512
- OpenAlex
- W2760946358
- Cited by
- 2,930
- References
- 71
- Citations by year
- 2026: 349 · 2025: 380 · 2024: 380 · 2023: 436 · 2022: 452 · 2021: 415 · 2020: 319 · 2019: 183 · 2018: 10 · 2017: 1 · 2014: 1
- FWCI
- 130.74
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Medicine (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NYU Langone Medical Center
- School of Medicine, New York University
- National Cancer Institute (P30CA016087)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
- Keywords
- non-small cell lung cancer, lung adenocarcinoma, squamous cell carcinoma, histopathological image classification, whole slide images, gene mutation prediction, STK11, EGFR, FAT1, SETBP1, KRAS, TP53, computer-aided diagnosis, frozen tissue, formalin-fixed paraffin-embedded tissue, biopsy
- MeSH terms
- Deep Learning, Adenocarcinoma, Carcinoma, Non-Small-Cell Lung, Carcinoma, Squamous Cell, Humans, Mutation, Neoplasm Proteins, Gene Expression Regulation, Neoplastic, Neural Networks, Computer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
