Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans
Open accessAbstract
This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features.
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?
SDAE, PGBM combined with RBM, and GoogLeNet CNN applied to breast US or shear-wave elastography images for breast cancer diagnosis obtained superior performance compared with human experts.
“In addition, the stacked denoising auto-encoder (SDAE) [88], a combination of the point-wise gated Boltzmann machine (PGBM) and RBM [89], and the GoogLeNet CNN [90] have been applied to breast US or shear-wave elastography images for breast cancer diagnosis; all of these obtained a superior performance when compared with human experts.”
From Deep Learning in Medical Ultrasound Analysis: A Review (2019) · cites this paper as [88]
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
STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT
Martin Bland, J., & Altman, D. (1986) · The Lancet · doi:10.1016/s0140-6736(86)90837-8 · Cited by 47,965 · see citing works
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260 · see citing works
Contour Detection and Hierarchical Image Segmentation
Arbeláez, P., Maire, M., Fowlkes, C. C., & Malik, J. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.161 · Cited by 5,472 · see citing works
(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953039 · Cited by 4,051 · see citing works
Armato, S. G., McLennan, G., Bidaut, L., McNitt‐Gray, M. F., Meyer, C. R., Reeves, A. P., Zhao, B., Aberle, D. R., Henschke, C. I., Hoffman, E. A., et al. (2011) · Medical Physics · doi:10.1118/1.3528204 · Cited by 2,887 · see citing works
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2013) · arXiv · doi:10.48550/arxiv.1312.6229 · Cited by 2,680 · see citing works
Distance Regularized Level Set Evolution and Its Application to Image Segmentation
Li, C., Xu, C., Gui, C., & Fox, M. D. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2069690 · Cited by 2,106 · see citing works
Computer-aided diagnosis in medical imaging: historical review, current status and future potential
Doi, K. (2007) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2007.02.002 · Cited by 1,855 · see citing works
Exploring Strategies for Training Deep Neural Networks
Larochelle, H., Bengio, Y., Louradour, J., & Lamblin, P. (2009) · doi:10.1145/1577069.1577070 · Cited by 1,109 · see citing works
Deep convolutional neural networks for multi-modality isointense infant brain image segmentation
Zhang, W., Li, R., Deng, H., Wang, L., Lin, W., Ji, S., & Shen, D. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.061 · Cited by 817 · see citing works
Improving Computer-Aided Detection UsingConvolutional Neural Networks and Random View Aggregation
Roth, H. R., Lu, L., Liu, J., Yao, J., Seff, A., Cherry, K., Kim, L., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2482920 · Cited by 608 · cited 1× · see citing works
“In the context of CAD, most works focused on the problem of abnormality detection (CADe) 36–38 .”
BackgroundLatent feature representation with stacked auto-encoder for AD/MCI diagnosis
Initiative, T. A. D. N., Suk, H., Lee, S., & Shen, D. G. (2013) · Brain Structure and Function · doi:10.1007/s00429-013-0687-3 · Cited by 523 · see citing works
Shin, H.-C., Orton, M. R., Collins, D. J., Doran, S. J., & Leach, M. O. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.277 · Cited by 488 · see citing works
Lung Image Database Consortium: Developing a Resource for the Medical Imaging Research Community
Armato, S. G., McLennan, G., McNitt‐Gray, M. F., Meyer, C. R., Yankelevitz, D. F., Aberle, D. R., Henschke, C. I., Hoffman, E. A., Kazerooni, E. A., MacMahon, H., et al. (2004) · Radiology · doi:10.1148/radiol.2323032035 · Cited by 381 · see citing works
Quantitative Analysis of Pulmonary Emphysema Using Local Binary Patterns
Srensen, L., Shaker, S. B., & de Bruijne, M. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2038575 · Cited by 368 · see citing works
Giger, M. L., Chan, H., & Boone, J. (2008) · Medical Physics · doi:10.1118/1.3013555 · Cited by 311 · see citing works
Computer-aided Diagnosis: How to Move from the Laboratory to the Clinic
van Ginneken, B., Schaefer-Prokop, C. M., & Prokop, M. (2011) · Radiology · doi:10.1148/radiol.11091710 · Cited by 306 · see citing works
Ciompi, F., de Hoop, B., van Riel, S. J., Chung, K., Scholten, E. T., Oudkerk, M., de Jong, P. A., Prokop, M., & Ginneken, B. V. (2015) · Medical Image Analysis · doi:10.1016/j.media.2015.08.001 · Cited by 289 · see citing works
Automated lung segmentation for thoracic CT
Armato, S. G., & Sensakovic, W. F. (2004) · Academic Radiology · doi:10.1016/j.acra.2004.06.005 · Cited by 259 · see citing works
Chen, C., Chou, Y., Han, K.-C., Hung, G.-S., Tiu, C., Chiou, H., & Chiou, S. (2003) · Radiology · doi:10.1148/radiol.2262011843 · Cited by 257 · see citing works
Joo, S., Yang, Y., Moon, W. K., & Kim, H. C. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.834617 · Cited by 255 · see citing works
Gómez‐Flores, W., Pereira, W. C. D. A., & Infantosi, A. F. C. (2012) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2012.2206398 · Cited by 248 · see citing works
Journey toward Computer-aided Diagnosis: Role of Image Texture Analysis
Tourassi, G. D. (1999) · Radiology · doi:10.1148/radiology.213.2.r99nv49317 · Cited by 233 · see citing works
Way, T. W., Hadjiiski, L. M., Sahiner, B., Chan, H., Cascade, P. N., Kazerooni, E. A., Bogot, N., & Zhou, C. (2006) · Medical Physics · doi:10.1118/1.2207129 · Cited by 225 · see citing works
Application of the mutual information criterion for feature selection in computer‐aided diagnosis
Tourassi, G. D., Frederick, E. D., Markey, M. K., & Floyd, C. E. (2001) · Medical Physics · doi:10.1118/1.1418724 · Cited by 222 · see citing works
Breast Image Analysis for Risk Assessment, Detection, Diagnosis, and Treatment of Cancer
Giger, M. L., Karssemeijer, N., & Schnabel, J. A. (2013) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071812-152416 · Cited by 211 · see citing works
Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors
Chang, R., Wu, W.-J., Moon, W. K., & Chen, D. (2005) · Breast Cancer Research and Treatment · doi:10.1007/s10549-004-2043-z · Cited by 198 · see citing works
Chen, H., Dou, Q., Ni, D., Cheng, J.-Z., Qin, J., Li, S., & Heng, P.-A. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-24553-9_62 · Cited by 180 · see citing works
Self-citationKubota, T., Jerebko, A. K., Dewan, M., Salganicoff, M., & Krishnan, A. V. (2010) · Medical Image Analysis · doi:10.1016/j.media.2010.08.005 · Cited by 178 · see citing works
Sahiner, B., Petrick, N., Chan, H., Hadjiiski, L. M., Paramagul, C., Helvie, M. A., & Gürcan, M. N. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.974922 · Cited by 174 · see citing works
Newell, D., Nie, K., Chen, J., Hsu, C., Yu, H. J., Nalcioğlu, O., & Su, M. (2009) · European Radiology · doi:10.1007/s00330-009-1616-y · Cited by 150 · see citing works
Way, T. W., Sahiner, B., Chan, H., Hadjiiski, L. M., Cascade, P. N., Chughtai, A. R., Bogot, N., & Kazerooni, E. A. (2009) · Medical Physics · doi:10.1118/1.3140589 · Cited by 147 · see citing works
Texture Classification Using Refined Histogram
Li, L., Tong, C. S., & Choy, S. K. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2041414 · Cited by 141 · see citing works
McCarville, M. B., Lederman, H. M., Santana, V. M., Daw, N. C., Shochat, S. J., Li, C., & Kaufman, R. A. (2006) · Radiology · doi:10.1148/radiol.2392050631 · Cited by 139 · see citing works
Sahiner, B., Chan, H., Roubidoux, M. A., Hadjiiski, L. M., Helvie, M. A., Paramagul, C., Bailey, J. E., Nees, A. V., & Blane, C. E. (2007) · Radiology · doi:10.1148/radiol.2423051464 · Cited by 135 · see citing works
Computer‐aided diagnosis in high resolution CT of the lungs
Sluimer, I. C., van Waes, P. F. G. M., Viergever, M. A., & van Ginneken, B. (2003) · Medical Physics · doi:10.1118/1.1624771 · Cited by 134 · see citing works
Tajbakhsh, N., Gotway, M., & Liang, J. (2015) · International Conference on Medical Image Computing and Computer-Assisted Intervention · doi:10.1007/978-3-319-24571-3_8 · Cited by 129 · see citing works
Alvarenga, A. V., Pereira, W. C. D. A., Infantosi, A. F. C., & de Azevedo, C. M. (2007) · Medical Physics · doi:10.1118/1.2401039 · Cited by 118 · see citing works
Computer‐aided diagnosis using morphological features for classifying breast lesions on ultrasound
Huang, Y. ‐., Chen, D. ‐., Jiang, Y., Kuo, S. ‐., Wu, H., & Moon, W. K. (2008) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.5205 · Cited by 106 · see citing works
Yang, M.-C., Moon, W. K., Wang, Y.-C. F., Bae, M. S., Huang, C., Chen, J., & Chang, R. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2279938 · Cited by 97 · see citing works
Awai, K., Murao, K., Ozawa, A., Nakayama, Y., Nakaura, T., Liu, D., Kawanaka, K., Funama, Y., Morishita, S., & Yamashita, Y. (2006) · Radiology · doi:10.1148/radiol.2383050167 · Cited by 85 · see citing works
Computer-Aided Diagnosis for Early-Stage Lung Cancer Based on Longitudinal and Balanced Data
Sun, T., Zhang, R., Wang, J., Li, X., & Guo, X. (2013) · PLoS ONE · doi:10.1371/journal.pone.0063559 · Cited by 49 · see citing works
Singh, S., Maxwell, J., Baker, J. A., Nicholas, J. L., & Lo, J. Y.-C. (2010) · Radiology · doi:10.1148/radiol.10081308 · Cited by 49 · see citing works
Automated Method for Improving System Performance of Computer-Aided Diagnosis in Breast Ultrasound
Drukker, K., Sennett, C. A., & Giger, M. L. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.928178 · Cited by 48 · see citing works
Computer-aided US Diagnosis of Breast Lesions by Using Cell-based Contour Grouping
Cheng, J., Chou, Y., Huang, C., Chang, Y., Tiu, C., Chen, K., & Chen, C. (2010) · Radiology · doi:10.1148/radiol.09090001 · Cited by 47 · see citing works
Chen, C., Chou, Y., Chen, C. S. K., Cheng, J.-Z., Ou, Y.-F., Yeh, F.-C. F., & Chen, K. (2005) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2005.09.011 · Cited by 46 · see citing works
Leveraging Mid-Level Semantic Boundary Cues for Automated Lymph Node Detection
Seff, A., Lu, L., Barbu, A., Roth, H., Shin, H.-C., & Summers, R. M. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-24571-3_7 · Cited by 36 · see citing works
ACCOMP: Augmented cell competition algorithm for breast lesion demarcation in sonography
Cheng, J., Chou, Y., Huang, C., Chang, Y., Tiu, C., Yeh, F.-C. F., Chen, K., Tsou, C., & Chen, C. (2010) · Medical Physics · doi:10.1118/1.3512799 · Cited by 26 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Vezhnevets V. & Konouchine V. GrowCut: Interactive multi-label ND image segmentation by cellular automata. Proc. of Graphicon. 1, 150– 156 (2005).
Cited by
Sorted by citations
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332 · see citing works
A guide to deep learning in healthcare
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0316-z · Cited by 5,312 · 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
Artificial intelligence in radiology
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018) · Nature Reviews Cancer · doi:10.1038/s41568-018-0016-5 · Cited by 3,803 · see citing works
Deep learning for healthcare: review, opportunities and challenges
Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx044 · Cited by 3,076 · see citing works
Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018) · Nature Medicine · doi:10.1038/s41591-018-0177-5 · Cited by 2,930 · see citing works
The practical implementation of artificial intelligence technologies in medicine
He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019) · Nature Medicine · doi:10.1038/s41591-018-0307-0 · Cited by 2,246 · see citing works
Deep Learning for Health Informatics
Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G.-Z. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2636665 · Cited by 2,000 · see citing works
The rise of deep learning in drug discovery
Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.01.039 · Cited by 1,770 · see citing works
Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges
Hesamian, M. H., Jia, W., He, X., & Kennedy, P. J. (2019) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-019-00227-x · Cited by 1,644 · see citing works
Deep Learning in Medical Imaging: General Overview
Lee, J., Jun, S., Cho, Y.-W., Lee, H., Kim, G. B., Seo, J. B., & Kim, N. (2017) · Korean Journal of Radiology · doi:10.3348/kjr.2017.18.4.570 · Cited by 1,287 · see citing works
Deep learning for brain MRI segmentation: state of the art and future directions
Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D. L., & Erickson, B. J. (2017) · Journal of Digital Imaging · doi:10.1007/s10278-017-9983-4 · Cited by 1,129 · see citing works
Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study
Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N. G., Venugopal, V. K., Mahajan, V., Rao, P., & Warier, P. (2018) · The Lancet · doi:10.1016/s0140-6736(18)31645-3 · Cited by 1,020 · see citing works
Applications of Deep Learning and Reinforcement Learning to Biological Data
Mahmud, M., Kaiser, M. S., Hussain, A., & Vassanelli, S. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2790388 · Cited by 852 · see citing works
Pesapane, F., Codari, M., & Sardanelli, F. (2018) · European Radiology Experimental · doi:10.1186/s41747-018-0061-6 · Cited by 839 · see citing works
Deep Learning in Medical Ultrasound Analysis: A Review
Liu, S., Wang, Y., Yang, X., Lei, B., Liu, L., Li, S. X., Ni, D., & Wang, T. (2019) · Engineering · doi:10.1016/j.eng.2018.11.020 · Cited by 798 · see citing works
Limkin, E. J., Sun, R., Dercle, L., Zacharaki, E. I., Robert, C., Reuzé, S., Schernberg, A., Nikos Paragios, A., Deutsch, É., & Ferté, C. (2017) · Annals of Oncology · doi:10.1093/annonc/mdx034 · Cited by 756 · see citing works
Rahimzadeh, M., & Attar, A. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100360 · Cited by 695 · see citing works
A critical review on computer vision and artificial intelligence in food industry
Kakani, V., Nguyen, V. H., Kumar, B. P., Kim, H., & Pasupuleti, V. R. (2020) · Journal of Agriculture and Food Research · doi:10.1016/j.jafr.2020.100033 · Cited by 516 · see citing works
Aung, Y. Y. M., Wong, D. C. S., & Ting, D. S. W. (2021) · British Medical Bulletin · doi:10.1093/bmb/ldab016 · Cited by 511 · see citing works
Deep Learning and Medical Diagnosis: A Review of Literature
Bakator, M., & Radosav, D. (2018) · Multimodal Technologies and Interaction · doi:10.3390/mti2030047 · Cited by 509 · see citing works
McBee, M. P., Awan, O. A. R., Colucci, A. T., Ghobadi, C. W., Kadom, N., Kansagra, A. P., Tridandapani, S., & Auffermann, W. F. (2018) · Academic Radiology · doi:10.1016/j.acra.2018.02.018 · Cited by 502 · see citing works
Artificial intelligence and machine learning for medical imaging: A technology review
Montero, A. M. B., Javaid, U., Valdés, G., Nguyen, D. T. D., Desbordes, P., Macq, B. M. M., Willems, S., Vandewinckele, L., Holmström, M., Löfman, F., et al. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.04.016 · Cited by 481 · see citing works
Convolutional neural network improvement for breast cancer classification
Ting, F. F., Tan, Y. J., & Sim, K. S. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.11.008 · Cited by 451 · see citing works
Bhandary, A., Prabhu, G. A., Rajinikanth, V., Thanaraj, K. P., Satapathy, S. C., Robbins, D. E., Shasky, C., Zhang, Y., Tavares, J. M. R. S., & Raja, N. S. M. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.11.013 · Cited by 450 · see citing works
Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy
Stephansen, J. B., Olesen, A. N., Olsen, M., Ambati, A., Leary, E. B., Moore, H. E., Carrillo, O., Lin, L., Han, F., Yan, H., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07229-3 · Cited by 414 · see citing works
Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review
Houssein, E. H., Emam, M. M., Ali, A. A., & Suganthan, P. N. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114161 · Cited by 410 · see citing works
Breast cancer detection using deep learning: Datasets, methods, and challenges ahead
Din, N. M. U., Dar, R. A., Rasool, M., & Assad, A. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106073 · Cited by 375 · see citing works
A deep learning framework for supporting the classification of breast lesions in ultrasound images
Han, S., Kang, H.-K., Jeong, J.-Y., Park, M.-H., Kim, W., Bang, W.-C., & Seong, Y.-K. (2017) · Physics in Medicine & Biology · doi:10.1088/1361-6560/aa82ec · Cited by 358 · see citing works
A survey on deep learning in medicine: Why, how and when?
Piccialli, F., Somma, V. D., Giampaolo, F., Cuomo, S., & Fortino, G. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.09.006 · Cited by 352 · see citing works
From Handcrafted to Deep-Learning-Based Cancer Radiomics: Challenges and Opportunities
Afshar, P., Mohammadi, A., Plataniotis, K. N., Oikonomou, A., & Benali, H. (2019) · IEEE Signal Processing Magazine · doi:10.1109/msp.2019.2900993 · Cited by 334 · see citing works
Triple-Negative Breast Cancer: A Review of Conventional and Advanced Therapeutic Strategies
Salcedo, J. M. M., Oza, G., Sharma, A. K., Arriaga, L., Hernández, J. M. H., Rotello, V. M., & Tapia-Ramı́rez, J. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17062078 · Cited by 337 · see citing works
Çelik, Y., Talo, M., Yıldırım, Ö., Karabatak, M., & Acharya, U. R. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.03.011 · Cited by 322 · see citing works
Rahimzadeh, M., Attar, A., & Sakhaei, S. M. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102588 · Cited by 315 · see citing works
Akkus, Z., Cai, J. C., Boonrod, A., Zeinoddini, A., Weston, A. D., Philbrick, K. A., & Erickson, B. J. (2019) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2019.06.004 · Cited by 324 · see citing works
Murtaza, G., Shuib, L., Abdul Wahab, A. W., Mujtaba, G., Mujtaba, G., Nweke, H. F., Al-garadi, M. A., Zulfiqar, F., Raza, G., & Azmi, N. A. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09716-5 · Cited by 342 · see citing works
Rahaman, M. M., Li, C., Yao, Y., Kulwa, F., Rahman, M. A., Wang, Q., Qi, S., Kong, F., Zhu, X., & Zhao, X. (2020) · Journal of X-Ray Science and Technology · doi:10.3233/xst-200715 · Cited by 302 · see citing works
Implementing Machine Learning in Radiology Practice and Research
Kohli, M. D., Prevedello, L. M. S., Filice, R. W., & Geis, J. R. (2017) · American Journal of Roentgenology · doi:10.2214/ajr.16.17224 · Cited by 301 · see citing works
Qureshi, I., Yan, J., Abbas, Q., Shaheed, K., Riaz, A. B., Wahid, A., Khan, M. W. J., & Szczuko, P. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.09.031 · Cited by 302 · see citing works
An overview of deep learning in the field of dentistry
Hwang, J. J., Jung, Y., Cho, B., & Heo, M.-S. (2019) · Imaging Science in Dentistry · doi:10.5624/isd.2019.49.1.1 · Cited by 297 · see citing works
Deep Learning Algorithms for Detection of Lymph Node Metastases From Breast Cancer
Golden, J. A. (2017) · JAMA · doi:10.1001/jama.2017.14580 · Cited by 297 · see citing works
Byra, M., Styczyński, G., Szmigielski, C. A., Kalinowski, P., Michałowski, Ł., Paluszkiewicz, R., Ziarkiewicz‐Wróblewska, B., Zieniewicz, K., Sobieraj, P., & Nowicki, A. (2018) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-018-1843-2 · Cited by 302 · see citing works
Miglani, A., & Kumar, N. (2019) · Vehicular Communications · doi:10.1016/j.vehcom.2019.100184 · Cited by 295 · see citing works
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions
Altaf, F., Islam, S. M. S., Akhtar, N., & Janjua, N. K. (2019) · IEEE Access · doi:10.1109/access.2019.2929365 · Cited by 293 · see citing works
Automatic breast ultrasound image segmentation: a survey
Xian, M., Zhang, Y., Cheng, H. D., Xu, F., Zhang, B., & Ding, J. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.02.012 · Cited by 284 · see citing works
“Deep learning: in the last several years, deep learning has demonstrated impressive performance for many tasks such as object recognition [158], image classification [159], semantic segmentation [160], facial expression recognition [163], speech recognition [164], medical applications [161, 170], etc.”
BackgroundBreast Cancer Classification From Histopathological Images Using Patch-Based Deep Learning Modeling
Hirra, I., Ahmad, M., Hussain, A., Ashraf, M. U., Saeed, I. A., Qadri, S. F., Alghamdi, A. M., & Alfakeeh, A. S. (2021) · IEEE Access · doi:10.1109/access.2021.3056516 · Cited by 277 · see citing works
Shen, Y., Shamout, F. E., Oliver, J. R., Witowski, J. S., Kannan, K., Park, J., Wu, N., Huddleston, C., Wolfson, S., Millet, A., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26023-2 · Cited by 278 · see citing works
Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review
Gardezi, S. J. S., Elazab, A., Lei, B., & Wang, T. (2019) · Journal of Medical Internet Research · doi:10.2196/14464 · Cited by 271 · see citing works
A deep learning method for classifying mammographic breast density categories
Mohamed, A. A., Berg, W. A., Peng, H., Luo, Y., Jankowitz, R. C., & Wu, S. (2017) · Medical Physics · doi:10.1002/mp.12683 · Cited by 266 · see citing works
Li, Z., Keel, S., Liu, C., He, Y., Meng, W., Scheetz, J., Lee, P. Y., Shaw, J., Ting, D., Wong, T. Y., et al. (2018) · Diabetes Care · doi:10.2337/dc18-0147 · Cited by 255 · see citing works
Physician perspectives on integration of artificial intelligence into diagnostic pathology
Sarwar, S., Dent, A., Faust, K., Richer, M., Djuric, U., Van Ommeren, R., & Diamandis, P. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0106-0 · Cited by 297 · see citing works
Byra, M., Galperin, M., Ojeda‐Fournier, H., Olson, L., O'Boyle, M., Comstock, C., & Andre, M. (2019) · Medical Physics · doi:10.1002/mp.13361 · Cited by 253 · see citing works
Wang, H., Shen, Y., Wang, S., Xiao, T., Deng, L., Wang, X., & Zhao, X. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.12.018 · Cited by 258 · see citing works
Qian, X., Pei, J., Zheng, H., Xie, X., Lin, Y., Zhang, H., Han, C., Gao, X., Zhang, H., Zheng, W., et al. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00711-2 · Cited by 253 · see citing works
FUIQA: Fetal Ultrasound Image Quality Assessment with Deep Convolutional Networks
Wu, L., Cheng, J.-Z., Li, S., Lei, B., Wang, T., & Ni, D. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2671898 · Cited by 242 · see citing works
Exploring the computational methods for protein-ligand binding site prediction
Zhao, J., Cao, Y., & Zhang, L. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.02.008 · Cited by 238 · see citing works
Automated diagnosis of breast ultrasonography images using deep neural networks
Qi, X., Zhang, L., Yao, C., Pi, Y., Chen, Y., Lv, Q., & Zhang, Y. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.12.006 · Cited by 233 · see citing works
From CNN to Transformer: A Review of Medical Image Segmentation Models
Yao, W., Bai, J., Liao, W., Chen, Y., Liu, M., & Xie, Y. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-00981-7 · Cited by 238 · see citing works
Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction
Nasser, M., & Yusof, U. K. (2023) · Diagnostics · doi:10.3390/diagnostics13010161 · Cited by 227 · 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
Yamanakkanavar, N., Choi, J. Y., & Lee, B. (2020) · Sensors · doi:10.3390/s20113243 · Cited by 225 · see citing works
Artificial intelligence in ultrasound
Shen, Y., Chen, L., Yue, W., & Xu, H. (2021) · European Journal of Radiology · doi:10.1016/j.ejrad.2021.109717 · Cited by 220 · see citing works
Understanding artificial intelligence based radiology studies: What is overfitting?
Mutasa, S., Sun, S., & Ha, R. (2020) · Clinical Imaging · doi:10.1016/j.clinimag.2020.04.025 · Cited by 216 · see citing works
Radiological images and machine learning: Trends, perspectives, and prospects
Zhang, Z., & Sejdić, E. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.02.017 · Cited by 236 · see citing works
Deep Learning Techniques to Diagnose Lung Cancer
Wang, L. (2022) · Cancers · doi:10.3390/cancers14225569 · Cited by 224 · see citing works
Highly accurate model for prediction of lung nodule malignancy with CT scans
Causey, J., Zhang, J., Ma, S., Jiang, B., Qualls, J., Politte, D. G., Prior, F., Zhang, S., & Huang, X. (2018) · Scientific Reports · doi:10.1038/s41598-018-27569-w · Cited by 214 · see citing works
Benefits and Risks of AI in Health Care: Narrative Review
Chustecki, M. (2024) · Interactive Journal of Medical Research · doi:10.2196/53616 · Cited by 208 · see citing works
“In addition, AI algorithms, such as deep learning, aid in disease classification and enable more personalized care [21].”
Vial, A., Stirling, D. A., Field, M., Ros, M., Ritz, C. H., Carolan, M., Holloway, L. C., & Miller, A. (2018) · Translational Cancer Research · doi:10.21037/tcr.2018.05.02 · Cited by 207 · see citing works
Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey
Huang, Q., Zhang, F., & Li, X. (2018) · BioMed Research International · doi:10.1155/2018/5137904 · Cited by 206 · see citing works
A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning
Gu, Y., Chi, J., Liu, J., Yang, L., Zhang, B., Yu, D., Zhao, Y., & Lu, X. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104806 · Cited by 203 · see citing works
Wang, X., Yang, W. H., Weinreb, J. C., Han, J., Li, Q., Kong, X., Yan, Y., Ke, Z., Luo, B., Liu, T., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-15720-y · Cited by 196 · see citing works
Deep learning in generating radiology reports: A survey
Monshi, M. M. A., Poon, J., & Chung, Y. Y. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101878 · Cited by 194 · see citing works
Precision medicine for human cancers with Notch signaling dysregulation (Review)
Katoh, M., & Katoh, M. (2019) · International Journal of Molecular Medicine · doi:10.3892/ijmm.2019.4418 · Cited by 196 · see citing works
VM-UNET-V2: Rethinking Vision Mamba UNet for Medical Image Segmentation
Zhang, M., Yu, Y., Sun, J., Gu, L., Ling, T., & Tao, X. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5128-0_27 · Cited by 186 · see citing works
An introduction to deep learning in medical physics: advantages, potential, and challenges
Shen, C., Nguyen, D. T. D., Zhou, Z., Jiang, S. B., Dong, B., & Jia, X. (2020) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab6f51 · Cited by 187 · see citing works
Agile convolutional neural network for pulmonary nodule classification using CT images
Zhao, X., Liu, L., Qi, S., Teng, Y., Li, J., & Qian, W. (2018) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-017-1696-0 · Cited by 188 · see citing works
Machine learning meets omics: applications and perspectives
Li, R., Li, L., Xu, Y., & Yang, J. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab460 · Cited by 180 · see citing works
Convolutional neural network-based PSO for lung nodule false positive reduction on CT images
da Silva, G. L. F., Valente, T. L. A., Silva, A. C., de Paiva, A. C., & Gattass, M. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.05.006 · Cited by 178 · see citing works
Lee, J.-S., Adhikari, S. P., Liu, L., Jeong, H.-G., Kim, H., & Yoon, S.-J. (2018) · Dentomaxillofacial Radiology · doi:10.1259/dmfr.20170344 · Cited by 178 · see citing works
Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET
Domingues, I., Pereira, G. C., Martins, P., Duarte, H., Santos, J. A. M., & Abreu, P. H. (2019) · Artificial Intelligence Review · doi:10.1007/s10462-019-09788-3 · Cited by 173 · see citing works
Burt, J. R., Torosdagli, N., Khosravan, N., RaviPrakash, H., Mortazi, A., Tissavirasingham, F., Hussein, S., & Bağcı, U. (2018) · British Journal of Radiology · doi:10.1259/bjr.20170545 · Cited by 171 · see citing works
Gao, J. A., Jiang, Q., Zhou, B., & Chen, D. (2019) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2019326 · Cited by 173 · see citing works
Intelligent Health Care: Applications of Deep Learning in Computational Medicine
Yang, S., Zhu, F., Ling, X., Liu, Q., & Zhao, P. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.607471 · Cited by 176 · see citing works
Cellular Senescence: From Mechanisms to Current Biomarkers and Senotherapies
Lucas, V., Cavadas, C., & Aveleira, C. A. (2023) · Pharmacological Reviews · doi:10.1124/pharmrev.122.000622 · Cited by 169 · see citing works
A Survey on Multimodal Data-Driven Smart Healthcare Systems: Approaches and Applications
Cai, Q., Wang, H. H., Li, Z., & Liu, X. (2019) · IEEE Access · doi:10.1109/access.2019.2941419 · Cited by 165 · see citing works
Shin, S. Y., Lee, S., Yun, I. D., Kim, S. M., & Lee, K. M. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2872031 · Cited by 160 · see citing works
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
Luo, L., Wang, X., Lin, Y., Ma, X., Tan, A., Chan, R., Vardhanabhuti, V., Chu, C. W., Cheng, K.-T. T., & Chen, H. (2024) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2024.3357877 · Cited by 174 · see citing works
Artificial intelligence, machine learning and the evolution of healthcare
Jones, L. D., Golan, D., Hanna, S. A., & Ramachandran, M. (2018) · Bone and Joint Research · doi:10.1302/2046-3758.73.bjr-2017-0147.r1 · Cited by 158 · see citing works
A survey on brain tumor detection techniques for MR images
Chahal, P. K., Pandey, S., & Goel, S. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08898-3 · Cited by 156 · see citing works
Deep Learning in Medical Ultrasound Image Analysis: A Review
Wang, Y., Ge, X., Ma, H., Qi, S., Zhang, G., & Yao, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3071301 · Cited by 162 · see citing works
Yan, K., Wang, X., Lu, L., Zhang, L., Harrison, A. P., Bagheri, M., & Summers, R. M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00965 · Cited by 150 · see citing works
Thrift, W. J., Ronaghi, S., Samad, M., Wei, H. L., Nguyen, D. G., Cabuslay, A. S., Groome, C. E., Santiago, P. J., Baldi, P., Hochbaum, A. I., et al. (2020) · ACS Nano · doi:10.1021/acsnano.0c05693 · Cited by 146 · see citing works
Interactive computer-aided diagnosis on medical image using large language models
Wang, S., Zhao, Z., Ouyang, X., Liu, T., Wang, Q., & Shen, D. G. (2024) · Communications Engineering · doi:10.1038/s44172-024-00271-8 · Cited by 149 · see citing works
Rapid Intraoperative Diagnosis of Pediatric Brain Tumors Using Stimulated Raman Histology
Hollon, T. C., Lewis, S., Pandian, B., Niknafs, Y. S., Garrard, M. R., Garton, H. J. L., Maher, C. O., McFadden, K. A., Snuderl, M., Lieberman, A. P., et al. (2017) · Cancer Research · doi:10.1158/0008-5472.can-17-1974 · Cited by 146 · see citing works
Deep learning for detection of focal epileptiform discharges from scalp EEG recordings
Tjepkema‐Cloostermans, M. C., de Carvalho, R. C. V., & van Putten, M. J. A. M. (2018) · Clinical Neurophysiology · doi:10.1016/j.clinph.2018.06.024 · Cited by 145 · see citing works
Artificial intelligence for breast cancer analysis: Trends & directions
Shah, S. M., Khan, R. A., Arif, S., & Sajid, U. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105221 · Cited by 154 · see citing works
Chen, S., Qin, J., Ji, X., Lei, B., Wang, T., Ni, D., & Cheng, J. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2629462 · Cited by 142 · see citing works
Polat, H., & Mehr, H. D. (2019) · Applied Sciences · doi:10.3390/app9050940 · Cited by 138 · see citing works
Convolution- and Attention-Based Neural Network for Automated Sleep Stage Classification
Zhu, T., Luo, W., & Yu, F. N. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17114152 · Cited by 138 · see citing works
Chen, C., Wang, Y., Niu, J., Liu, X., Li, Q., & Gong, X. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3078370 · Cited by 138 · see citing works
- Authors
- Cheng, J.-Z., Ni, D., Chou, Y.-H., Qin, J., Tiu, C.-M., Chang, Y.-C., Huang, C.-S., Shen, D., & Chen, C.-M.
- Affiliations
- Shenzhen University (CN)
- Taipei Veterans General Hospital (TW)
- National Taiwan University (TW)
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Chou, Yi-Hong
- Shen, Dinggang
- Chen, Chung-Ming
- ORCID
- Venue
- Scientific Reports
- Published
- 2016-04-15
- Volume
- 6
- Issue
- 1
- Pages
- 24454-24454
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC4832199
- OpenAlex
- W2341106171
- Cited by
- 755
- References
- 48
- Citations by year
- 2026: 9 · 2025: 32 · 2024: 78 · 2023: 59 · 2022: 100 · 2021: 120 · 2020: 103 · 2019: 110 · 2018: 87 · 2017: 45 · 2016: 11
- FWCI
- 68.59
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- UNC Libraries
- Funders
- National Science Council (NSC 100)
- Shenzhen Fundamental Research Program (JCYJ20130329105033277, JCYJ20150525092940982, 81571758, JCYJ20140509172609164, JSE201109150013A)
- National Cancer Institute (R01CA206100)
- 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
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- computer-aided diagnosis, stacked denoising autoencoder, pulmonary nodules, benign versus malignant differentiation, medical image denoising, feature extraction, noise immunity, differential diagnosis
- MeSH terms
- Machine Learning, Algorithms, Breast, Solitary Pulmonary Nodule, Diagnosis, Computer-Assisted, Female, Humans, Tomography, X-Ray Computed, Ultrasonography, Reproducibility of Results, Datasets as Topic
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
