Topic · AI in cancer detection
Most cited papers in AI in cancer detection
What are the most cited papers in AI in cancer detection?
The most cited paper in AI in cancer detection is "A survey on deep learning in medical image analysis" (Geert J. S. Litjens, Thijs Kooi, Babak Ehteshami Bejnordi et al., 2017), with 15,309 citations. Next come "pROC: an open-source package for R and S+ to analyze and compare ROC curves" (14,550) and "Dermatologist-level classification of skin cancer with deep neural networks" (14,423).
Showing 50 of 100 papers. Every paper with more than 1,113 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | A survey on deep learning in medical image analysis Geert J. S. Litjens, Thijs Kooi, Babak Ehteshami Bejnordi et al. · Medical Image Analysis | 26 Jul 2017 | 15,309 |
| 2 | pROC: an open-source package for R and S+ to analyze and compare ROC curves Xavier Robin, Natacha Turck, Alexandre Hainard et al. · BMC Bioinformatics | 17 Mar 2011 | 14,550 |
| 3 | Dermatologist-level classification of skin cancer with deep neural networks Andre Esteva, Brett Kuprel, Roberto A. Novoa et al. · Nature | 25 Jan 2017 | 14,423 |
| 4 | A survey on Image Data Augmentation for Deep Learning Connor Shorten, Taghi M. Khoshgoftaar · Journal Of Big Data | 6 Jul 2019 | 13,152 |
| 5 | QuPath: Open source software for digital pathology image analysis Peter Bankhead, Maurice Bernard Loughrey, José A. Fernández et al. · Scientific Reports | 28 Nov 2017 | 9,140 |
| 6 | Radiomics: Images Are More than Pictures, They Are Data Robert J. Gillies, Paul E. Kinahan, Hedvig Hricak · Radiology | 18 Nov 2015 | 8,616 |
| 7 | Unified segmentation John Ashburner, Karl John Friston · NeuroImage | 2 Apr 2005 | 7,633 |
| 8 | The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) Bjoern H. Menze et al. · IEEE Transactions on Medical Imaging | 4 Dec 2014 | 6,843 |
| 9 | Radiomics: Extracting more information from medical images using advanced feature analysis Philippe Lambin, Emmanuel Rios-Velazquez, Ralph T. H. Leijenaar et al. · European Journal of Cancer | 16 Jan 2012 | 6,202 |
| 10 | Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes Joel S. Parker, Michael Eugene Mullins, Maggie C.U. Cheang et al. · Journal of Clinical Oncology | 9 Feb 2009 | 4,974 |
| 11 | Deep Learning in Medical Image Analysis Dinggang G. Shen, Guorong Wu, Heung‐Il Suk · Annual Review of Biomedical Engineering | 16 Mar 2017 | 4,947 |
| 12 | Convolutional neural networks: an overview and application in radiology Rikiya Yamashita, Mizuho Nishio, Richard Kinh Gian et al. · Insights into Imaging | 22 Jun 2018 | 4,717 |
| 13 | Convolutional networks for images, speech, and time series Yann LeCun, Yoshua Bengio · HAL (Le Centre pour la Communication Scientifique Directe) | Oct 1998 | 4,363 |
| 14 | Medical image analysis: progress over two decades and the challenges ahead James S. Duncan, Nicholas Ayache · IEEE Transactions on Pattern Analysis and Machine Intelligence | 2000 | 4,166 |
| 15 | The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping Alex Zwanenburg et al. · Radiology | 10 Mar 2020 | 4,159 |
| 16 | Artificial intelligence in radiology Ahmed Hosny, Chintan Parmar, John Quackenbush et al. · Nature reviews. Cancer | 17 May 2018 | 3,801 |
| 17 | ilastik: interactive machine learning for (bio)image analysis Stuart Berg, Dominik Kutra, Thorben Kroeger et al. · Nature Methods | 30 Sep 2019 | 3,760 |
| 18 | How To Build and Interpret a Nomogram for Cancer Prognosis Alexia E. Iasonos, Deborah Schrag, Ganesh V. Raj et al. · Journal of Clinical Oncology | 6 Mar 2008 | 3,461 |
| 19 | Machine learning applications in cancer prognosis and prediction Konstantina D. Kourou, Themis P. Exarchos, Konstantinos Exarchos et al. · Computational and Structural Biotechnology Journal | 15 Nov 2014 | 3,415 |
| 20 | Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer Babak Ehteshami Bejnordi et al. · JAMA | 12 Dec 2017 | 3,405 |
| 21 | International evaluation of an AI system for breast cancer screening Scott Mayer McKinney et al. · Nature | 2020 | 3,314 |
| 22 | Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? Nima Tajbakhsh, Jae Y. Shin, Suryakanth Gurudu et al. · IEEE Transactions on Medical Imaging | 7 Mar 2016 | 3,309 |
| 23 | beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework Irina Higgins, Löıc Matthey, Arka Pal et al. · International Conference on Learning Representations | 24 Apr 2017 | 3,076 |
| 24 | Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning Nicolas Coudray, Paolo Ocampo, Theodore Sakellaropoulos et al. · Nature Medicine | 6 Sep 2018 | 3,013 |
| 25 | Clinical-grade computational pathology using weakly supervised deep learning on whole slide images Gabriele S. V. Campanella, Matthew G. Hanna, Luke Geneslaw et al. · Nature Medicine | 15 Jul 2019 | 2,845 |
| 26 | Segment anything in medical images Jun Ma, Yuting He, Feifei Li et al. · Nature Communications | 22 Jan 2024 | 2,830 |
| 27 | Automated detection of COVID-19 cases using deep neural networks with X-ray images Tülin Öztürk, Muhammed Talo, Eylul Azra Yildirim et al. · Computers in Biology and Medicine | 28 Apr 2020 | 2,702 |
| 28 | Laser Capture Microdissection Michael R. Emmert‐Buck, Robert F. Bonner, Paul David Smith et al. · Science | 8 Nov 1996 | 2,575 |
| 29 | H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes Xiaomeng Li, Hao Chen, Xiaojuan Qi et al. · IEEE Transactions on Medical Imaging | 11 Jun 2018 | 2,526 |
| 30 | Mammographic Density and the Risk and Detection of Breast Cancer Norman F. Boyd, Helen Guo, Lisa Joy Martin et al. · New England Journal of Medicine | 17 Jan 2007 | 2,510 |
| 31 | Support vector machine classification and validation of cancer tissue samples using microarray expression data Terrence S. Furey, Nello Cristianini, Nigel Duffy et al. · Bioinformatics | Oct 2000 | 2,475 |
| 32 | Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks Ioannis D. Apostolopoulos, Tzani A. Mpesiana · Physical and Engineering Sciences in Medicine | 3 Apr 2020 | 2,435 |
| 33 | MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation Nabil Ibtehaz, M. Sohel Rahman · Neural Networks | 4 Sep 2019 | 2,356 |
| 34 | Current Methods in Medical Image Segmentation Dzung L. Pham, Chenyang Xu, Jerry L. Prince · Annual Review of Biomedical Engineering | Aug 2000 | 2,349 |
| 35 | Opportunities and obstacles for deep learning in biology and medicine Travers H. Ching et al. · Journal of The Royal Society Interface | Apr 2018 | 2,322 |
| 36 | The practical implementation of artificial intelligence technologies in medicine Jianxing He, Sally L. Baxter, Jie Xu et al. · Nature Medicine | 24 Dec 2018 | 2,319 |
| 37 | CE-Net: Context Encoder Network for 2D Medical Image Segmentation Zaiwang Gu, Jun Cheng, Huazhu Fu et al. · IEEE Transactions on Medical Imaging | 7 Mar 2019 | 2,282 |
| 38 | Simultaneous Truth and Performance Level Estimation (STAPLE): An Algorithm for the Validation of Image Segmentation Simon Keith Warfield, Kelly H. Zou, William M. Wells · IEEE Transactions on Medical Imaging | Jul 2004 | 2,100 |
| 39 | Histopathological Image Analysis: A Review Metin N. Gürcan, Laura E. Boucheron, Ali Can et al. · IEEE Reviews in Biomedical Engineering | 2009 | 2,075 |
| 40 | Quantification of histochemical staining by color deconvolution. Arnout C. C. Ruifrok, D. A. Johnston · PubMed | Aug 2001 | 2,072 |
| 41 | U-Net: deep learning for cell counting, detection, and morphometry Thorsten Falk et al. · Nature Methods | 4 Dec 2018 | 2,057 |
| 42 | Deep Learning for Health Informatics Daniele Ravì, Charence Wong, Fani Deligianni et al. · IEEE Journal of Biomedical and Health Informatics | 29 Dec 2016 | 2,025 |
| 43 | GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification Maayan Frid-Adar, Idit Diamant, Eyal Klang et al. · Neurocomputing | 21 Sep 2018 | 1,987 |
| 44 | U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications Nahian Alam Siddique, Sidike Paheding, Colin P. Elkin et al. · IEEE Access | 2021 | 1,962 |
| 45 | Diagnostic Performance of Digital versus Film Mammography for Breast-Cancer Screening Etta D. Pisano, Constantine A. Gatsonis, Edward R. Hendrick et al. · New England Journal of Medicine | 16 Sep 2005 | 1,945 |
| 46 | A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis Xiaoxuan Liu, Livia Faes, Aditya U. Kale et al. · The Lancet Digital Health | 25 Sep 2019 | 1,936 |
| 47 | Deformable models in medical image analysis: a survey Tim McInerney, Demetri Terzopoulos · Medical Image Analysis | Jun 1996 | 1,921 |
| 48 | Generative adversarial network in medical imaging: A review Yi Xin, Ekta Walia, Paul Babyn · Medical Image Analysis | 31 Aug 2019 | 1,911 |
| 49 | Artificial intelligence in cancer imaging: Clinical challenges and applications Wenya Linda Bi, Ahmed Hosny, Matthew B. Schabath et al. · CA A Cancer Journal for Clinicians | 5 Feb 2019 | 1,886 |
| 50 | Causability and explainability of artificial intelligence in medicine Andreas Holzinger, Georg Langs, Helmut Denk et al. · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery | 2 Apr 2019 | 1,872 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.