Most cited papers in Radiomics and Machine Learning in Medical Imaging
What are the most cited papers in Radiomics and Machine Learning in Medical Imaging?
The most cited paper in Radiomics and Machine Learning in Medical Imaging 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 "High-performance medicine: the convergence of human and artificial intelligence" (10,125) and "UNet++: A Nested U-Net Architecture for Medical Image Segmentation" (9,545).
Showing 50 of 100 papers. Every paper with more than 1,309 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 | High-performance medicine: the convergence of human and artificial intelligence Eric J. Topol · Nature Medicine | 28 Dec 2018 | 10,125 |
| 3 | UNet++: A Nested U-Net Architecture for Medical Image Segmentation Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh et al. · Lecture notes in computer science | 2018 | 9,545 |
| 4 | 3D Slicer as an image computing platform for the Quantitative Imaging Network Andriy Fedorov, Reinhard Beichel, Jayashree Kalpathy–Cramer et al. · Magnetic Resonance Imaging | 7 Jul 2012 | 9,292 |
| 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 | scikit-image: image processing in Python Stéfan van der Walt, Johannes L. Schönberger, Juan Nunez-Iglesias et al. · PeerJ | 19 Jun 2014 | 7,267 |
| 8 | Computational Radiomics System to Decode the Radiographic Phenotype Joost J. M. van Griethuysen, Andriy Fedorov, Chintan Parmar et al. · Cancer Research | 31 Oct 2017 | 6,776 |
| 9 | Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): a randomised controlled trial Roy S. Herbst, Paul Baas, Dong‐Wan Kim et al. · The Lancet | 19 Dec 2015 | 6,590 |
| 10 | ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking Matthew D. Wilkerson, David Neil Hayes · Bioinformatics | 28 Apr 2010 | 6,383 |
| 11 | TNM Classification of Malignant Tumors Darryl Carter · The American Journal of Surgical Pathology | Oct 1998 | 6,356 |
| 12 | 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 |
| 13 | Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning Hoo-Chang Shin, Holger R. Roth, Mingchen Gao et al. · IEEE Transactions on Medical Imaging | 11 Feb 2016 | 5,901 |
| 14 | Radiomics: the bridge between medical imaging and personalized medicine Philippe Lambin, Ralph T. H. Leijenaar, Timo M. Deist et al. · Nature Reviews Clinical Oncology | 4 Oct 2017 | 5,839 |
| 15 | Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach Hugo J.W.L. Aerts, Emmanuel Rios Velazquez, Ralph T. H. Leijenaar et al. · Nature Communications | 3 Jun 2014 | 5,287 |
| 16 | A reproducible evaluation of ANTs similarity metric performance in brain image registration Brian Avants, Nicholas J. Tustison, Gang Song et al. · NeuroImage | 18 Sep 2010 | 5,258 |
| 17 | Deep Learning in Medical Image Analysis Dinggang G. Shen, Guorong Wu, Heung‐Il Suk · Annual Review of Biomedical Engineering | 16 Mar 2017 | 4,947 |
| 18 | Artificial intelligence in healthcare: past, present and future Fei Jiang, Yong Jiang, Hui Zhi et al. · Stroke and Vascular Neurology | 21 Jun 2017 | 4,877 |
| 19 | 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 |
| 20 | elastix: A Toolbox for Intensity-Based Medical Image Registration Stefan Klein, Marius Staring, Keelin Murphy et al. · IEEE Transactions on Medical Imaging | 23 Nov 2009 | 4,426 |
| 21 | UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh et al. · IEEE Transactions on Medical Imaging | 13 Dec 2019 | 4,396 |
| 22 | Modified RECIST (mRECIST) Assessment for Hepatocellular Carcinoma Riccardo Antonio Lencioni, Josep Maria Llovet · Seminars in Liver Disease | Feb 2010 | 4,376 |
| 23 | The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping Alex Zwanenburg et al. · Radiology | 10 Mar 2020 | 4,159 |
| 24 | An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics Jianfang Liu et al. · Cell | Apr 2018 | 4,058 |
| 25 | Swin-Unet: Unet-Like Pure Transformer for Medical Image Segmentation Hu Cao, Yueyue Wang, Joy Chen et al. · Lecture notes in computer science | 2023 | 4,036 |
| 26 | Updated Response Assessment Criteria for High-Grade Gliomas: Response Assessment in Neuro-Oncology Working Group Patrick Yung Wen, David R. Macdonald, David A. Reardon et al. · Journal of Clinical Oncology | 16 Mar 2010 | 3,884 |
| 27 | Artificial intelligence in radiology Ahmed Hosny, Chintan Parmar, John Quackenbush et al. · Nature reviews. Cancer | 17 May 2018 | 3,801 |
| 28 | Reduced Lung-Cancer Mortality with Volume CT Screening in a Randomized Trial Harry J. de Koning et al. · New England Journal of Medicine | 29 Jan 2020 | 3,674 |
| 29 | ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases Xiaosong Wang, Yifan Peng, Le Lu et al. | Jul 2017 | 3,497 |
| 30 | FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0 Ronald Boellaard et al. · European Journal of Nuclear Medicine and Molecular Imaging | Dec 2014 | 3,406 |
| 31 | Pembrolizumab versus chemotherapy for previously untreated, PD-L1-expressing, locally advanced or metastatic non-small-cell lung cancer (KEYNOTE-042): a randomised, open-label, controlled, phase 3 trial Tony S.K. Mok et al. · The Lancet | 4 Apr 2019 | 3,392 |
| 32 | International evaluation of an AI system for breast cancer screening Scott Mayer McKinney et al. · Nature | 2020 | 3,314 |
| 33 | 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 |
| 34 | COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images Linda Wang, Zhong Qiu Lin, Alexander M. Wong · Scientific Reports | 11 Nov 2020 | 3,190 |
| 35 | Nivolumab plus Ipilimumab in Lung Cancer with a High Tumor Mutational Burden Matthew David Hellmann et al. · New England Journal of Medicine | 16 Apr 2018 | 3,185 |
| 36 | UNETR: Transformers for 3D Medical Image Segmentation Ali Hatamizadeh, Yucheng Tang, Vishwesh Nath et al. · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) | 2022 | 3,181 |
| 37 | Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic Timothy An-thy Chan, Mark M. Yarchoan, Elizabeth M. Jaffee et al. · Annals of Oncology | Nov 2018 | 2,932 |
| 38 | The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans Samuel G. Armato et al. · Medical Physics | 24 Jan 2011 | 2,896 |
| 39 | UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation Huimin Huang, Lanfen Lin, Ruofeng Tong et al. | 9 Apr 2020 | 2,801 |
| 40 | Neoadjuvant Nivolumab plus Chemotherapy in Resectable Lung Cancer Patrick M. Forde et al. · New England Journal of Medicine | 11 Apr 2022 | 2,732 |
| 41 | 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 |
| 42 | CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2014–2018 Quinn T. Ostrom, Gino Cioffi, Kristin A. Waite et al. · Neuro-Oncology | Oct 2021 | 2,612 |
| 43 | First-Line Nivolumab in Stage IV or Recurrent Non–Small-Cell Lung Cancer David Paul Carbone et al. · New England Journal of Medicine | 21 Jun 2017 | 2,602 |
| 44 | Early Lung Cancer Action Project: overall design and findings from baseline screening Claudia I. Henschke, Dorothy I. McCauley, David F. Yankelevitz et al. · The Lancet | Jul 1999 | 2,535 |
| 45 | Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017 Heber MacMahon, David P. Naidich, Jin Mo Goo et al. · Radiology | Mar 2017 | 2,516 |
| 46 | 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 |
| 47 | Response criteria for phase II studies of supratentorial malignant glioma. David R. Macdonald, T L Cascino, S. Clifford Schold et al. · Journal of Clinical Oncology | Jul 1990 | 2,428 |
| 48 | World Health Organization classification of tumors Paul Kleihues, Leslie H. Sobin · Cancer | 15 Jun 2000 | 2,381 |
| 49 | Early and locally advanced non-small-cell lung cancer (NSCLC): ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up Pieter E. Postmus, Keith M. Kerr, Matthijs Oudkerk et al. · Annals of Oncology | 5 May 2017 | 2,371 |
| 50 | MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation Nabil Ibtehaz, M. Sohel Rahman · Neural Networks | 4 Sep 2019 | 2,356 |
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.