The DOI matches its Crossref record; 13 of the 15 references it cites were found in a registry or an index.
The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository
Open accessAbstract
The National Institutes of Health have placed significant emphasis on sharing of research data to support secondary research. Investigators have been encouraged to publish their clinical and imaging data as part of fulfilling their grant obligations. Realizing it was not sufficient to merely ask investigators to publish their collection of imaging and clinical data, the National Cancer Institute (NCI) created the open source National Biomedical Image Archive software package as a mechanism for centralized hosting of cancer related imaging. NCI has contracted with Washington University in Saint Louis to create The Cancer Imaging Archive (TCIA)-an open-source, open-access information resource to support research, development, and educational initiatives utilizing advanced medical imaging of cancer. In its first year of operation, TCIA accumulated 23 collections (3.3 million images). Operating and maintaining a high-availability image archive is a complex challenge involving varied archive-specific resources and driven by the needs of both image submitters and image consumers. Quality archives of any type (traditional library, PubMed, refereed journals) require management and customer service. This paper describes the management tasks and user support model for TCIA.
Overview
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
Verification
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?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“we used the recent adrenocortical carcinoma CT dataset34,42,43, where the segmentation target, adrenal tumor, was neither part of the training nor of the existing validation sets.”
From Segment anything in medical images (2024) · cites this paper as [34]
Experimental — accuracy not yet measuredIn the citing paper
“They are beginning to be solved through sharing of oncology metadata (www.oriencancer.org or the Children’s Oncology Group), and some sharing of image files has been enabled by The Cancer Imaging Archive (77).”
From Radiomics: Images Are More than Pictures, They Are Data (2016) · cites this paper as [77]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Human Connectome Project: A data acquisition perspective
Essen, D. C. V., Uǧurbil, K., Auerbach, E. J., Barch, D. M., Behrens, T. E. J., Bucholz, R. D., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., et al. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.02.018 · Cited by 2,638
Self-citationHowe, D. G., Costanzo, M. C., Fey, P., Gojobori, T., Hannick, L. I., Hide, W., Hill, D. P., Kania, R., Schaeffer, M. L., Pierre, S. S., et al. (2008) · Nature · doi:10.1038/455047a · Cited by 701
Informatics and Data Mining Tools and Strategies for the Human Connectome Project
Marcus, D. S., Harwell, J., Olsen, T., Hodge, M. R., Glasser, M. F., Prior, F., Jenkinson, M., Laumann, T. O., Curtiss, S. W., & Essen, D. C. V. (2011) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2011.00004 · Cited by 700
Self-citationBenson, D. A., Boguski, M. S., Lipman, D. J., & Ostell, J. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.1.1 · Cited by 171
Keator, D. B., Grethe, J. S., Marcus, D., Özyurt, İ. B., Gadde, S., Murphy, S., Pieper, S. D., Greve, D. N., Notestine, R. J., Bockholt, H. J., et al. (2008) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2008.917893 · Cited by 123
Grethe, J. S., Baru, C., Gupta, A., James, M., Ludaescher, B., Martone, M. E., Papadopoulos, P. M., Peltier, S. T., Rajasekar, A., Santini, S., et al. (2005) · PubMed · Cited by 80
Birney, E., Bateman, A. G., Clamp, M. E., & Hubbard, T. (2001) · Nature · doi:10.1038/35057004 · Cited by 63
Quantitative Imaging for Evaluation of Response to Cancer Therapy
Clarke, L. P., Croft, B. S., Nordstrom, R. J., Zhang, H., Kelloff, G. J., & Tatum, J. L. (2009) · Translational Oncology · doi:10.1593/tlo.09217 · Cited by 46
Hampton, T. (2006) · JAMA · doi:10.1001/jama.296.16.1958-d · Cited by 45
Vendt, B. A., McKinstry, R. C., Ball, W. S., Kraut, M. A., Prior, F., BARTON, B. A., Casella, J. F., & DeBaun, M. R. (2008) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-008-9114-3 · Cited by 31
Self-citation
Cited by
Sorted by citations
Items 601–700 of 1,012
Mammography and breast tomosynthesis simulator for virtual clinical trials
Badal, A., Sharma, D., Graff, C. G., Zeng, R., & Badano, A. G. (2020) · Computer Physics Communications · doi:10.1016/j.cpc.2020.107779 · Cited by 55
Sood, R., Shao, W., Kunder, C. A., Teslovich, N. C., Wang, J. B., Soerensen, S. J. C., Madhuripan, N., Jawahar, A., Brooks, J. D., Ghanouni, P., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101957 · Cited by 55
Zhu, W., Huang, Y., Tang, H., Qian, Z. Y., Du, N., Fan, W., & Xie, X. S. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/392969 · Cited by 53
Trebeschi, S., Bodalal, Z., Boellaard, T. N., Bucho, T. M. T., Drago, S. G., Kurilova, I., Calin-Vainak, A. M., Pizzi, A. D., Muller, M., Hummelink, K., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.609054 · Cited by 56
Zhong, Y. J., Yang, Y., Fang, Y., Wang, J., & Hu, W. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.638197 · Cited by 54
Caballo, M., Sanderink, W. B. G., Han, L., Gao, Y., Athanasiou, A., & Mann, R. M. (2022) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.28273 · Cited by 54
Agnes, S. A., Anitha, J., & Solomon, A. A. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106059 · Cited by 56
Lung Cancer Detection Model Using Deep Learning Technique
Sait, A. R. W. (2023) · Applied Sciences · doi:10.3390/app132212510 · Cited by 63
Brain Tumor Detection and Classification from Multi-sequence MRI: Study Using ConvNets
Banerjee, S., Mitra, S., Masulli, F., & Stefano Rovetta, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11723-8_17 · Cited by 54
Classification of Lung Nodules Based on Deep Residual Networks and Migration Learning
Wu, P., Sun, X., Zhao, Z., Wang, H., Pan, S., & Schuller, B. W. (2020) · Computational Intelligence and Neuroscience · doi:10.1155/2020/8975078 · Cited by 54
Shiri, I., Sadr, A. V., Akhavan, A., Salimi, Y., Sanaat, A., Amini, M., Razeghi, B., Saberi, A., Arabi, H., Ferdowsi, S., et al. (2022) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-022-06053-8 · Cited by 60
Sharing Biomedical Data: Strengthening AI Development in Healthcare
Pereira, T., Morgado, J., Silva, F., Pelter, M. M., Dias, V. R., Barros, R., Freitas, C., Negrão, E., Flor de Lima, B., Correia da Silva, M., et al. (2021) · Healthcare · doi:10.3390/healthcare9070827 · Cited by 54
“The Cancer Imaging Archive (TCIA) hosts a large archive of medical images of cancer accessible for public download, containing MRI, CT, digital histopathology, etc, and supporting image-related data such as patient outcomes, treatment details, genomics, and expert analyses [66].”
BackgroundIs Segmentation Uncertainty Useful?
Czolbe, S., Arnavaz, K., Krause, O., & Feragen, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-78191-0_55 · Cited by 53
Shiri, I., Sadr, A. V., Amini, M., Salimi, Y., Sanaat, A., Akhavanallaf, A., Razeghi, B., Ferdowsi, S., Saberi, A., Arabi, H., et al. (2022) · Clinical Nuclear Medicine · doi:10.1097/rlu.0000000000004194 · Cited by 56
Ding, K., Zhou, M., Wang, H., Zhang, S., & Metaxas, D. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00168-6 · Cited by 53
Zhou, Q., Huang, Z., Ding, M., & Zhang, X. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3241439 · Cited by 58
Diffusion probabilistic priors for zero‐shot low‐dose CT image denoising
Liu, X., Xie, Y., Liu, C., Cheng, J., Diao, S., Tan, S., & Liang, X. (2024) · Medical Physics · doi:10.1002/mp.17431 · Cited by 71
The new era of quantitative cell imaging—challenges and opportunities
Bagheri, N., Carpenter, A. E., Lundberg, E. K., Plant, A. L., & Horwitz, R. (2022) · Molecular Cell · doi:10.1016/j.molcel.2021.12.024 · Cited by 66
Deep learning-based artificial intelligence for prostate cancer detection at biparametric MRI
Mehralivand, S., Yang, D., Harmon, S. A., Xu, D., Xu, Z., Roth, H. R., Masoudi, S., Kesani, D., Lay, N. S., Merino, M. J., et al. (2022) · Abdominal Radiology · doi:10.1007/s00261-022-03419-2 · Cited by 56
Akilandeswari, A., Sungeetha, D., Joseph, C., Thaiyalnayaki, K., Baskaran, K., Rajabathar, J. R., Al‐Lohedan, H. A., Aldhayan, D. M., Muthusamy, K., & Hadish, K. M. (2022) · Evidence-based Complementary and Alternative Medicine · doi:10.1155/2022/3415603 · Cited by 52
Severn, C., Suresh, K., Görg, C., Choi, Y. S., Jain, R., & Ghosh, D. (2022) · Sensors · doi:10.3390/s22145205 · Cited by 58
Mokoatle, M., Marivate, V. N., Mapiye, D. S., Bornman, R. M. S., & Hayes, V. M. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05235-x · Cited by 63
Extended Modality Propagation: Image Synthesis of Pathological Cases
Cordier, N., Delingette, H., Lê, M., & Ayache, N. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2589760 · Cited by 52
Chen, X., Zhou, Z., Hannan, R., Thomas, K. M., Pedrosa, I., Kapur, P., Brugarolas, J., Mou, X., & Wang, J. (2018) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aae5cd · Cited by 54
Yogananda, C. G. B., Shah, B. R., Yu, F. F., Pinho, M. C., Nalawade, S. S., Murugesan, G. K., Wagner, B., Mickey, B., Patel, T., Fei, B., et al. (2020) · Neuro-Oncology Advances · doi:10.1093/noajnl/vdaa066 · Cited by 52
Ibrahim, A., Refaee, T., Primakov, S. P., Barufaldi, B., Acciavatti, R. J., Granzier, R. W. Y., Hustinx, R., Mottaghy, F. M., Woodruff, H. C., Wildberger, J. E., et al. (2021) · Cancers · doi:10.3390/cancers13081848 · Cited by 52
Shiri, I., Arabi, H., Sanaat, A., Jenabi, E., Becker, M., & Zaidi, H. (2021) · Clinical Nuclear Medicine · doi:10.1097/rlu.0000000000003789 · Cited by 52
Leitheiser, M., Capper, D., Seegerer, P., Lehmann, A., Schüller, U., Müller, K., Klauschen, F., Jurmeister, P. S., & Bockmayr, M. L. (2021) · The Journal of Pathology · doi:10.1002/path.5845 · Cited by 51
Fu, Y., Dong, S., Niu, M., Xue, L., Guo, H., Huang, Y., Xu, Y., Yu, T., Shi, K., Yang, Q., et al. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102787 · Cited by 51
Radiomics for characterization of the glioma immune microenvironment
Khalili, N., Kazerooni, A. F., Familiar, A., Haldar, D., Kraya, A., Foster, J., Koptyra, M., Storm, P. B., Resnick, A. C., & Nabavizadeh, A. (2023) · npj Precision Oncology · doi:10.1038/s41698-023-00413-9 · Cited by 51
Electrical Impedance Tomography Guided by Digital Twins and Deep Learning for Lung Monitoring
Zhu, L., Lu, W., Soleimani, M., Li, Z., & Zhang, M. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3298389 · Cited by 51
Engwall, E., Glimelius, L., & Hynning, E. (2018) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aabb7b · Cited by 51
Lung Cancer Tumor Region Segmentation Using Recurrent 3D-DenseUNet
Kamal, U., Rafi, A. M., Hoque, R., Wu, Q. M. J., & Hasan, M. K. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-62469-9_4 · Cited by 61
ADMM-based deep reconstruction for limited-angle CT
Wang, J., Zeng, L., Wang, C., & Guo, Y. (2019) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab1aba · Cited by 52
Reliability of tumor segmentation in glioblastoma: Impact on the robustness of MRI‐radiomic features
Tixier, F., Um, H., Young, R. J., & Veeraraghavan, H. (2019) · Medical Physics · doi:10.1002/mp.13624 · Cited by 51
Bashir, U., Kawa, B., Siddique, M., Mak, S. M., Nair, A., McLean, E., Billè, A., Goh, V., & Cook, G. (2019) · British Journal of Radiology · doi:10.1259/bjr.20190159 · Cited by 51
Khorrami, M., Bera, K., Leo, P., Vaidya, P., Patil, P. D., Thawani, R., Velu, P., Rajiah, P. S., Alilou, M., Choi, H., et al. (2020) · Lung Cancer · doi:10.1016/j.lungcan.2020.02.018 · Cited by 51
Capsule Network with Its Limitation, Modification, and Applications—A Survey
Haq, M. U., Sethi, M. A. J., & Rehman, A. U. (2023) · Machine Learning and Knowledge Extraction · doi:10.3390/make5030047 · Cited by 54
Impact of Imaging Biomarkers and AI on Breast Cancer Management: A Brief Review
Saleh, G. A., Batouty, N. M., Gamal, A., Elnakib, A. A., Hamdy, O., Sharafeldeen, A., Mahmoud, A. H., Ghazal, M. A., Yousaf, J., Alhalabi, M. T., et al. (2023) · Cancers · doi:10.3390/cancers15215216 · Cited by 50
Choi, H., & Na, K. J. (2018) · Theranostics · doi:10.7150/thno.23767 · Cited by 51
Integrating deep and radiomics features in cancer bioimaging
Bizzego, A., Bussola, N., Salvalai, D., Chierici, M., Maggio, V., Jurman, G., & Furlanello, C. (2019) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb.2019.8791473 · Cited by 56
Brain structural disorders detection and classification approaches: a review
Bhatele, K. R., & Bhadauria, S. S. (2019) · Artificial Intelligence Review · doi:10.1007/s10462-019-09766-9 · Cited by 50
Koçak, B., Durmaz, E. Ş., Kaya, O. K., & Kılıçkesmez, Ö. (2019) · Acta Radiologica · doi:10.1177/0284185119881742 · Cited by 50
Christie, J. R., Lang, P., Zelko, L. M., Palma, D. A., Abdelrazek, M., & Mattonen, S. A. (2020) · Canadian Association of Radiologists Journal · doi:10.1177/0846537120941434 · Cited by 56
Deep learning-based medical image segmentation with limited labels
Chi, W., Ma, L., Wu, J., Chen, M., Lu, W., & Gu, X. (2020) · Physics in Medicine and Biology · doi:10.1088/1361-6560/abc363 · Cited by 53
Comes, M. C., Forgia, D. L., Didonna, V., Fanizzi, A., Giotta, F., Latorre, A., Martinelli, E., Mencattini, A., Paradiso, A., Tamborra, P., et al. (2021) · Cancers · doi:10.3390/cancers13102298 · Cited by 52
Kiser, K. J., Barman, A., Stieb, S., Fuller, C. D., & Giancardo, L. (2021) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-021-00460-3 · Cited by 49
Kaur, A., Chauhan, A. P. S., & Aggarwal, A. K. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115686 · Cited by 50
Kriegsmann, K., Lobers, F., Zgorzelski, C., Kriegsmann, J., Janßen, C., Meliß, R. R., Muley, T., Sack, U., Steinbuss, G., & Kriegsmann, M. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.1022967 · Cited by 50
RadioPathomics: Multimodal learning in non-small cell lung cancer for adaptive radiotherapy
Tortora, M., Cordelli, E., Sicilia, R., Nibid, L., Ippolito, E., Perrone, G., Ramella, S., & Soda, P. (2023) · IEEE Access · doi:10.1109/access.2023.3275126 · Cited by 49
Learning From Ambiguous Labels for Lung Nodule Malignancy Prediction
Liao, Z., Xie, Y., Hu, S., & Xia, Y. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3149344 · Cited by 49
Shen, Z., Ding, F., Jolfaei, A., Yadav, K., Vashisht, S., & Yu, K. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3190765 · Cited by 49
Al‐Kababji, A., Bensaali, F., Dakua, S. P., & Himeur, Y. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105532 · Cited by 55
Revisiting nnU-Net for Iterative Pseudo Labeling and Efficient Sliding Window Inference
Huang, Z., Wang, H., Ye, J., Niu, J., Tu, C., Yang, Y., Du, S., Deng, Z., Gu, L., & He, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-23911-3_16 · Cited by 49
Ye, Z., Saraf, A., Ravipati, Y., Hoebers, F. J. P., Catalano, P. J., Zha, Y., Zapaishchykova, A., Likitlersuang, J., Guthier, C. V., Tishler, R. B., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.28280 · Cited by 50
A Review of Deep Learning CT Reconstruction From Incomplete Projection Data
Wang, T., Xia, W., Lu, J., & Zhang, Y. (2023) · IEEE Transactions on Radiation and Plasma Medical Sciences · doi:10.1109/trpms.2023.3316349 · Cited by 53
Artificial intelligence links CT images to pathologic features and survival outcomes of renal masses
Xiong, Y., Yao, L., Lin, J., Yao, J., Bai, Q., Huang, Y., Zhang, X., Huang, R., Wang, R., Wang, K., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56784-z · Cited by 49
Hossain, S., Najeeb, S., Shahriyar, A., Abdullah, Z. R., & Haque, M. A. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683802 · Cited by 49
Dai, Z., Carver, E. N., Liu, C. X., Lee, J., Feldman, A. M., Zong, W., Pantelic, M., Elshaikh, M. A., & Wen, N. W. (2020) · Advances in Radiation Oncology · doi:10.1016/j.adro.2020.01.005 · Cited by 50
Analyzing magnetic resonance imaging data from glioma patients using deep learning
Menze, B. H., Isensee, F., Wiest, R., Wiestler, B., Maier-Hein, K. H., Reyes, M., & Bakas, S. (2020) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2020.101828 · Cited by 51
Fèvre, C. L., Constans, J., Chambrelant, I., Antoni, D., Bund, C., Leroy‐Freschini, B., Schött, R., Cébula, H., & Noël, G. (2021) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2021.103230 · Cited by 50
Kartasalo, K., Bulten, W., Delahunt, B., Chen, P.-H. C., Pinckaers, H., Olsson, H., Ji, X., Mulliqi, N., Samaratunga, H., Tsuzuki, T., et al. (2021) · European Urology Focus · doi:10.1016/j.euf.2021.07.002 · Cited by 53
Deep learning in CT colonography: differentiating premalignant from benign colorectal polyps
Wesp, P., Grosu, S., Graser, A., Maurus, S., Schulz, C., Knösel, T., Fabritius, M. P., Schachtner, B. M., Yeh, B. M., Cyran, C. C., et al. (2022) · European Radiology · doi:10.1007/s00330-021-08532-2 · Cited by 48
Radici, L., Ferrario, S., Borca, V. C., Cante, D., Paolini, M., Piva, C., Baratto, L., Franco, P., & Porta, M. R. L. (2022) · Life · doi:10.3390/life12122088 · Cited by 48
Machine learning based multipurpose medical image watermarking
Sinhal, R., & Ansari, I. A. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08457-5 · Cited by 48
Pathology-and-Genomics Multimodal Transformer for Survival Outcome Prediction
Ding, K., Zhou, M., Metaxas, D., & Zhang, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43987-2_60 · Cited by 48
Elsayed, B., Elhadary, M. R., Elshoeibi, R. M., Elshoeibi, A. M., Badr, A. A., Metwally, O. H., elsherif, R., Salem, M. E., Khadadah, F. M., Alshurafa, A., et al. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1330977 · Cited by 48
Al-Tam, R. M., Al-Hejri, A. M., Alshamrani, S. S., Al–antari, M. A., & Narangale, S. M. (2024) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2024.08.007 · Cited by 48
3-D Active Contour Segmentation Based on Sparse Linear Combination of Training Shapes (SCoTS)
Farhangi, M. M., Frigui, H., Seow, A., & Amini, A. A. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2720119 · Cited by 48
Nakao, M., Imanishi, K., Ueda, N., Imai, Y., Kirita, T., & Matsuda, T. (2020) · IEEE Access · doi:10.1109/access.2020.3002090 · Cited by 48
Deep Learning data integration for better risk stratification models of bladder cancer.
Poirion, O., Chaudhary, K., & Garmire, L. X. (2018) · Europe PMC (PubMed Central) · Cited by 48
Data-driven translational prostate cancer research: from biomarker discovery to clinical decision
Lin, Y., Zhao, X. J., Miao, Z., Ling, Z., Wei, X., Pu, J., Hou, J., & Shen, B. (2020) · Journal of Translational Medicine · doi:10.1186/s12967-020-02281-4 · Cited by 49
Preoperative CT and survival data for patients undergoing resection of colorectal liver metastases
Simpson, A. L., Peoples, J., Creasy, J. M., Gabor Fichtinger, A., Gangai, N. C., Murthy, K. N. K., Lassó, A., Shia, J., D'Angelica, M. I., & Gian, R. K. (2024) · Scientific Data · doi:10.1038/s41597-024-02981-2 · Cited by 48
ReMIND: The Brain Resection Multimodal Imaging Database
Juvekar, P., Dorent, R., Kögl, F., Torio, E., Barr, C., Rigolo, L., Galvin, C., Jowkar, N., Kazi, A., Haouchine, N., et al. (2024) · Scientific Data · doi:10.1038/s41597-024-03295-z · Cited by 48
V-Shaped Dense Denoising Convolutional Neural Network for Electrical Impedance Tomography
Zhang, X., Wang, Z., Fu, R., Wang, D., Chen, X., Guo, X., & Wang, H. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3166177 · Cited by 48
Wang, F., Cheng, C., Cao, W., Wu, Z., Wang, H., Wei, W., Yan, Z., & Liu, Z. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106657 · Cited by 47
Attention guided neural ODE network for breast tumor segmentation in medical images
Ru, J., Lu, B., Chen, B., Shi, J., Chen, G., Wang, M., Pan, Z., Lin, Y., Gao, Z., Zhou, J., et al. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106884 · Cited by 49
Kazmierski, M., Welch, M. L., Kim, S., McIntosh, C., Rey‐McIntyre, K., Huang, S. H., Patel, T., Tadic, T., Milosevic, M., Liu, F., et al. (2023) · Cancer Research Communications · doi:10.1158/2767-9764.crc-22-0152 · Cited by 46
Machine Learning‐Based Lung Cancer Detection Using Multiview Image Registration and Fusion
Nazir, I., Haq, I. U., AlQahtani, S. A., Khan, M. M., & Dahshan, M. H. (2023) · Journal of Sensors · doi:10.1155/2023/6683438 · Cited by 47
DeepTensor: Low-Rank Tensor Decomposition With Deep Network Priors
Saragadam, V., Balestriero, R., Veeraraghavan, A., & Baraniuk, R. G. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3450575 · Cited by 53
Ullah, M. S., Khan, M. A., Albarakati, H. M., Damaševičius, R., & Alsenan, S. A. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109183 · Cited by 47
Abraham, B., & Nair, P. (. ). M. S. (2018) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2018.06.009 · Cited by 48
Hudson, A. L., Parker, N., Khong, P., Parkinson, J., Dwight, T., Ikin, R. J., Zhu, Y., Chen, J., Wheeler, H., & Howell, V. M. (2018) · Frontiers in Oncology · doi:10.3389/fonc.2018.00314 · Cited by 46
Astaraki, M., Wang, C., Buizza, G., Toma-Daşu, I., Lazzeroni, M., & Smedby, Ö. (2019) · Physica Medica · doi:10.1016/j.ejmp.2019.03.024 · Cited by 47
Al-Saffar, Z. A., & Yıldırım, T. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.105945 · Cited by 47
Apostolopoulos, I. D., Papathanasiou, N. D., & Panayiotakis, G. (2021) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2021.08.006 · Cited by 47
T., J. M. C., Arif, M. H., Starmans, M. P. A., Niessen, W. J., Bangma, C. H., Schoots, I. G., & Veenland, J. F. (2021) · Cancers · doi:10.3390/cancers14010012 · Cited by 47
Bianconi, F., Fravolini, M. L., Pizzoli, S., Palumbo, I., Minestrini, M., Rondini, M., Nuvoli, S. M. F., Spanu, A., & Palumbo, B. (2021) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-20-1356 · Cited by 47
A novel adaptive momentum method for medical image classification using convolutional neural network
Aytaç, U. C., Güneş, A., & Ajlouni, N. (2022) · BMC Medical Imaging · doi:10.1186/s12880-022-00755-z · Cited by 50
Zhang, Z., Wang, Z., Luo, T., Yan, M., Dekker, A. L. A. J., Ruysscher, D. D., Traverso, A., Wee, L., & Zhao, L. (2023) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2023.109581 · Cited by 46
MultiTalent: A Multi-dataset Approach to Medical Image Segmentation
Ulrich, C., Isensee, F., Wald, T., Zenk, M., Baumgartner, M., & Maier-Hein, K. H. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43898-1_62 · Cited by 46
da Silva, H. E. C., Santos, G. N. M., Leite, A. F., Mesquita, C. R. M., Figueiredo, P. T. D. S., Stefani, C. M., & de Melo, N. S. (2023) · PLoS ONE · doi:10.1371/journal.pone.0292063 · Cited by 61
Identifying BAP1 Mutations in Clear-Cell Renal Cell Carcinoma by CT Radiomics: Preliminary Findings
Feng, Z., Zhang, L., Zhong, Q., Shen, Q., Hu, Z., & Chen, F. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.00279 · Cited by 46
Ferreira, J. R., Oliveira, M. C., & de Azevedo‐Marques, P. M. (2016) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-016-9894-9 · Cited by 45
Carter, L. M., Crawford, T. M., Sato, T., Furuta, T., Choi, C., Kim, C. H., Brown, J. L., Bolch, W. E., ZANZONICO, P. B., & Lewis, J. S. (2019) · Journal of Nuclear Medicine · doi:10.2967/jnumed.119.229013 · Cited by 45
Pati, S., Verma, R., Akbari, H., Bilello, M., Hill, V. B., Sako, C., Correa, R., Beig, N. G., Venet, L., Thakur, S. P., et al. (2020) · Medical Physics · doi:10.1002/mp.14556 · Cited by 45
Predicting cell invasion in breast tumor microenvironment from radiological imaging phenotypes
Arefan, D., Hausler, R., Sumkin, J., Sun, M., & Wu, S.-D. (2021) · BMC Cancer · doi:10.1186/s12885-021-08122-x · Cited by 28
Zhao, Q., Zhong, L., Xiao, J., Zhang, J., Chen, Y., Liao, W., Zhang, S., & Wang, G. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3262680 · Cited by 45
A novel deep learning-based technique for detecting prostate cancer in MRI images
Singh, S. K., Sinha, A., Singh, H., Mahanti, A., Patel, A., Mahajan, S., Pandit, A. K., & Varadarajan, V. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15793-0 · Cited by 45
Artificial intelligence in breast imaging: Current situation and clinical challenges
You, C., Shen, Y., Sun, S., Zhou, J., Li, J., Su, G.-H., Michalopoulou, E., Peng, W., Gu, Y., Guo, W., et al. (2023) · Exploration · doi:10.1002/exp.20230007 · Cited by 45
Metadata
- Authors
- Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F.
- Affiliations
- Washington University in St. Louis (US)
- Frederick National Laboratory for Cancer Research (US)
- Corresponding author
- Clark, Kenneth
- Venue
- Journal of Digital Imaging
- Published
- 2013-07-25
- Volume
- 26
- Issue
- 6
- Pages
- 1045-1057
- ISSN
- 0897-1889 (ISSN-L)
- 1618-727X
- PMC
- PMC3824915
- OpenAlex
- W2083927153
- Cited by
- 4,599
- References
- 15
- Citations by year
- 2026: 337 · 2025: 584 · 2024: 685 · 2023: 667 · 2022: 623 · 2021: 542 · 2020: 418 · 2019: 320 · 2018: 204 · 2017: 112 · 2016: 68 · 2015: 27 · 2014: 7 · 2013: 1
- FWCI
- 101.32
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- other-oa
- www.springer.com/tdm (tdm)
- Locations
- Journal of Imaging Informatics in Medicine (publishedVersion, other-oa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. Department of Health and Human Services (HHSN261200800001E)
- Radiological Society of North America
- National Institutes of Health (Contract No. HHSN261200800001E., #HHSN261200800001E, HHSN261200800001C)
- National Cancer Institute (No. HHSN261200800001E, Contract No.,HHSN261200800001E, HHSN261200800001E))
- Topics
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Social Sciences › Sociology and Political Science › Data Analysis and Archiving
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- The Cancer Imaging Archive, medical image repositories, tumor imaging, open source software, data sharing, secondary research, repository management, user support
- MeSH terms
- Diagnostic Imaging, Female, Humans, Male, Medical Informatics, Neoplasms, Quality Control, Radiology Information Systems, Software, United States, Program Evaluation, Information Storage and Retrieval, National Cancer Institute (U.S.), Multimodal Imaging
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