Deep learning based classification of focal liver lesions with contrast-enhanced ultrasound

LIBSVM: A library for support vector machines

Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918

Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008

Ferlay, J., Shin, H., Bray, F., Forman, D., Mathers, C. D., & Parkin, D. M. (2010) · International Journal of Cancer · doi:10.1002/ijc.25516 · Cited by 21,468

Reducing the dimensionality of data with neural networks

Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928

A fast learning algorithm for deep belief nets

Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336

Libsvm : A library for support vector machines

Ferrari, V. (2008) · Medical Entomology and Zoology · Cited by 10,123

Learning Deep Architectures for AI

Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,316

Proceedings of the 25th international conference on Machine learning - ICML '08

(2008) · doi:10.1145/1390156 · Cited by 6,865

Learning Deep Architectures for AI

Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 5,049

Training Products of Experts by Minimizing Contrastive Divergence

Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879

A Practical Guide to Training Restricted Boltzmann Machines

Hinton, G. E. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_32 · Cited by 2,852

Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier]

Arel, I., Rose, D. C., & Karnowski, T. P. (2010) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2010.938364 · Cited by 1,252

Guidelines and Good Clinical Practice Recommendations for Contrast Enhanced Ultrasound (CEUS) in the Liver – Update 2012

Claudon, M., Dietrich, C. F., Choi, B. I., Cosgrove, D. O., Kudo, M., Nolsøe, C., Piscaglia, F., Wilson, S. R., Barr, R. G., Chammas, M. C., et al. (2012) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2012.09.002 · Cited by 969

Untitled work

(2009) · neural information processing systems · Cited by 962

Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis

Kim, H., & Park, H. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm134 · Cited by 865

Circulating Tumor Cells versus Imaging—Predicting Overall Survival in Metastatic Breast Cancer

Budd, G. T., Cristofanilli, M., Ellis, M. J., Stopeck, A., Borden, E. C., Miller, M. C., Matera, J., Repollet, M., Doyle, G. V., Terstappen, L. W. M. M., et al. (2006) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-05-1769 · Cited by 776

Systematic 5 Region Prostate Biopsy is Superior to Sextant Method for Diagnosing Carcinoma of the Prostate

Eskew, L. A., Bare, R. L., & McCullough, D. L. (1997) · The Journal of Urology · doi:10.1016/s0022-5347(01)65322-9 · Cited by 765

Cancer Screening in the United States, 2010: A Review of Current American Cancer Society Guidelines and Issues in Cancer Screening

Smith, R. A., Cokkinides, V. E., Brooks, D. D., Saslow, D., & Brawley, O. W. (2010) · CA A Cancer Journal for Clinicians · doi:10.3322/caac.20063 · Cited by 659

Sentinel Lymph Node Biopsy in Breast Cancer

Veronesi, U., Viale, G., Paganelli, G., Zurrida, S., Luini, A., Galimberti, V. E., Veronesi, P., Intra, M., Maisonneuve, P., Zucca, F., et al. (2010) · Annals of Surgery · doi:10.1097/sla.0b013e3181c0e92a · Cited by 554

Guidelines and Good Clinical Practice Recommendations for Contrast Enhanced Ultrasound (CEUS) in the Liver – Update 2012

Claudon, M., Dietrich, C. F., Choi, B. I., Cosgrove, D. O., Kudo, M., Nolsøe, C., Piscaglia, F., Wilson, S. R., Barr, R., Chammas, M. C., et al. (2012) · Ultraschall in der Medizin - European Journal of Ultrasound · doi:10.1055/s-0032-1325499 · Cited by 554

Characterization of Focal Liver Lesions with Contrast-specific US Modes and a Sulfur Hexafluoride–filled Microbubble Contrast Agent: Diagnostic Performance and Confidence

Quaia, E., Calliada, F., Bertolotto, M., Rossi, S. M., Garioni, L., de la Rosa, L. E. S., & Mucelli, R. P. (2004) · Radiology · doi:10.1148/radiol.2322031401 · Cited by 477

Deep Learning and Its Applications to Signal and Information Processing [Exploratory DSP

Yu, D., & Deng, L. (2010) · IEEE Signal Processing Magazine · doi:10.1109/msp.2010.939038 · Cited by 459

On the quantitative analysis of deep belief networks

Salakhutdinov, R., & Murray, I. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390266 · Cited by 411

Selected techniques for data mining in medicine

Lavrač, N. (1999) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(98)00062-1 · Cited by 356

Large-scale Learning with SVM and Convolutional for Generic Object Categorization

Huang, F. J., & LeCun, Y. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.164 · Cited by 326

Deep Machine Learning—A New Frontier in Artificial Intelligence Research

Arel, I., Rose, D. C., & Karnowski, T. P. (2010) · Cited by 314

Synergistic Face Detection and Pose Estimation with Energy-Based Models

Osadchy, M., LeCun, Y., & Miller, M. L. (2006) · Lecture notes in computer science · doi:10.1007/11957959_10 · Cited by 214

Advances in Neural Information Processing Systems 21 (NIPS 2008)

Schweikert, G., Rätsch, G., Widmer, C., & Schölkopf, B. (2008) · Cited by 212

Handling of Dynamic Sequences in Nuclear Medicine

Paola, R. D., Bazin, J., Aubry, F., Aurengo, A., Cavaillolès, F., Herry, J. Y., & Kahn, E. (1982) · IEEE Transactions on Nuclear Science · doi:10.1109/tns.1982.4332188 · Cited by 197

The use of principal components in the quantitative analysis of gamma camera dynamic studies

Barber, D. C. (1980) · Physics in Medicine and Biology · doi:10.1088/0031-9155/25/2/008 · Cited by 188

Deep Learning and Its Applications to Signal and Information Processing

Yu, D., & Deng, L. (2011) · IEEE Signal Processing Magazine · Cited by 159

Face Detection Using Convolutional Neural Networks and Gabor Filters

Kwolek, B. (2005) · Lecture notes in computer science · doi:10.1007/11550822_86 · Cited by 114

Perfusion magnetic resonance imaging of the liver

Thng, C. H. (2010) · World Journal of Gastroenterology · doi:10.3748/wjg.v16.i13.1598 · Cited by 112

Quantitative contrast enhanced ultrasound of the liver for time intensity curves—Reliability and potential sources of errors

Ignee, A., Jedrejczyk, M., Schuessler, G., Jakubowski, W. S., & Dietrich, C. F. (2009) · European Journal of Radiology · doi:10.1016/j.ejrad.2008.10.016 · Cited by 111

Contrast-Enhanced Sonography Helps in Discrimination of Benign From Malignant Adnexal Masses

Marret, H., Sauget, S., Giraudeau, B., Brewer, M., Ranger‐Moore, J., Body, G., & Tranquart, F. (2004) · Journal of Ultrasound in Medicine · doi:10.7863/jum.2004.23.12.1629 · Cited by 106

Staging and diagnosis of non‐small cell lung cancer: Invasive modalities

Yasufuku, K., & Fujisawa, T. (2007) · Respirology · doi:10.1111/j.1440-1843.2007.01035.x · Cited by 88

Focal liver lesions: contrast-enhanced ultrasound

Bartolotta, T. V., Taibbi, A., Midiri, M., & Lagalla, R. (2008) · Abdominal Imaging · doi:10.1007/s00261-008-9378-6 · Cited by 84

Parametric imaging for characterizing focal liver lesions in contrast-enhanced ultrasound

Rognin, N. G., Arditi, M., Mercier, L., Frinking, P. J. A., Schneider, M., Perrenoud, G., Anaye, A., Meuwly, J., & Tranquart, F. (2010) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2010.1716 · Cited by 81

Computer‐aided diagnosis for the classification of focal liver lesions by use of contrast‐enhanced ultrasonography

Shiraishi, J., Sugimoto, K., Moriyasu, F., Kamiyama, N., & Doi, K. (2008) · Medical Physics · doi:10.1118/1.2900109 · Cited by 71

A Scalable Formulation of Probabilistic Linear Discriminant Analysis: Applied to Face Recognition

Shafey, L. E., McCool, C., Wallace, R., & Marcel, S. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.38 · Cited by 71

Contrast-enhanced ultrasonography parameters in neural network diagnosis of liver tumors

Streba, C. T. (2012) · World Journal of Gastroenterology · doi:10.3748/wjg.v18.i32.4427 · Cited by 66

A data mining approach for fall detection by using k -nearest neighbour algorithm on wireless sensor network data

Erdoğan, Ş. Z., & Bilgin, T. T. (2012) · IET Communications · doi:10.1049/iet-com.2011.0228 · Cited by 64

Fast Classification of Meat Spoilage Markers Using Nanostructured ZnO Thin Films and Unsupervised Feature Learning

Längkvist, M., Coradeschi, S., Loutfi, A., & Rayappan, J. B. B. (2013) · Sensors · doi:10.3390/s130201578 · Cited by 52

Contrast-enhanced ultrasound for diagnosis of prostate cancer and kidney lesions

Mitterberger, M., Pelzer, A. E., Colleselli, D., Bartsch, G. H., Strasser, H., Pallwein, L., Aigner, F., Gradl, J., & Frauscher, F. (2007) · European Journal of Radiology · doi:10.1016/j.ejrad.2007.07.027 · Cited by 52

A posteriorirespiratory gating in contrast ultrasound for assessment of hepatic perfusion

Renault, G., Tranquart, F., Perlbarg, V., Bleuzen, A., Herment, A., & Frouin, F. (2005) · Physics in Medicine and Biology · doi:10.1088/0031-9155/50/19/003 · Cited by 48

Convolutional networks for speech detection

Sukittanon, S., Surendran, A. C., Platt, J. C., & Burges, C. J. C. (2004) · Interspeech (USB) · doi:10.21437/interspeech.2004-376 · Cited by 45

Software-based quantification of contrast-enhanced ultrasound in focal liver lesions—A feasibility study

Goertz, R. S., Bernatik, T., Strobel, D. D., Hahn, E. G., & Haendl, T. (2009) · European Journal of Radiology · doi:10.1016/j.ejrad.2009.11.004 · Cited by 31

Contrast-enhanced ultrasound for detection and diagnosis of renal clear cell carcinoma.

Dong, X., Shen, Y., Xu, L., Chun-mei, X., Bi, W., & Wang, X. (2009) · PubMed · Cited by 31

Respiratory motion correction in free-breathing ultrasound image sequence for quantification of hepatic perfusion.

Zhang, J., Ding, M.-Y., Meng, F., Yuchi, M., & Zhang, X.-M. (2011) · Medical Physics (Lancaster) · doi:10.1118/1.3606456 · Cited by 27

Hepatic Perfusion Imaging Using Factor Analysis of Contrast Enhanced Ultrasound

Lueck, G., Kim, T. K., Burns, P. N., & Martel, A. L. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.922695 · Cited by 26

Quantification of enhancement of focal liver lesions during contrast-enhanced ultrasound (CEUS). Analysis of ten selected frames is more simple but as reliable as the analysis of the entire loop for most parameters

Salvatore, V., Borghi, A., Sagrini, E., Galassi, M., Gianstefani, A., Bolondi, L., & Piscaglia, F. (2011) · European Journal of Radiology · doi:10.1016/j.ejrad.2011.01.097 · Cited by 22

Automating tumor classification with pixel-by-pixel contrast-enhanced ultrasound perfusion kinetics

Ta, C., Kono, Y., Barback, C. V., Mattrey, R. F., & Kummel, A. C. (2012) · Journal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · doi:10.1116/1.3692962 · Cited by 17

Influence of radionuclide adsorption on detection efficiency and energy resolution for flow-cell radiation detectors

DeVol, T. A., Keillor, M. E., & Burggraf, L. W. (1996) · IEEE Transactions on Nuclear Science · doi:10.1109/23.507056 · Cited by 17

Quantitative Evaluation of Two-Factor Analysis Applied to Hepatic Perfusion Study Using Contrast-enhanced Ultrasound

Zhang, J., Ding, M., Meng, F., & Zhang, X. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2171038 · Cited by 16

Contrast‐Enhanced Doppler Perfusion Index

Ramnarine, K. V., Leen, E. L. S., Oppo, K., Angerson, W. J., & McArdle, C. S. (2002) · Journal of Ultrasound in Medicine · doi:10.7863/jum.2002.21.10.1121 · Cited by 15

Liver magnetic resonance imaging: State of the art

Sijens, P. E. (2010) · World Journal of Gastroenterology · doi:10.3748/wjg.v16.i13.1558 · Cited by 7

Sparse non-negative matrix factorizations for ultrasound factor analysis

Chen, X., Wu, K., Ding, M., & Sang, N. (2013) · Optik · doi:10.1016/j.ijleo.2013.04.048 · Cited by 3

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

Deep learning-based breast cancer classification through medical imaging modalities: State of the art and research challenges

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

A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence–Powered Ultrasound for Improving Clinical Workflow

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

Machine learning for medical ultrasound: status, methods, and future opportunities

Brattain, L. J., Telfer, B. A., Dhyani, M., Grajo, J. R., & Samir, A. E. (2018) · Abdominal Radiology · doi:10.1007/s00261-018-1517-0 · Cited by 262

Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques

Das, A., Acharya, U. R., Panda, S. S., & Sabut, S. (2018) · Cognitive Systems Research · doi:10.1016/j.cogsys.2018.12.009 · Cited by 200

An Automated Method for Classifying Liver Lesions in Contrast-Enhanced Ultrasound Imaging Based on Deep Learning Algorithms

Mămuleanu, M., Urhuț, M. C., Săndulescu, L., Kamal, C., Pătrașcu, A.-M., Ionescu, A. G., Şerbănescu, M.-S., & Streba, C. T. (2023) · Diagnostics · doi:10.3390/diagnostics13061062 · Cited by 8

Kernel sparse representation based classification of focal liver lesions with contrast-enhanced ultrasound

Li, D., Yakui, Z., & Jin, J. (2017) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc.2017.7969968 · Cited by 1

Malignancy diagnosis of liver lesion in contrast enhanced ultrasound using an end-to-end method based on deep learning

Zhou, H., Ding, J., Zhou, Y., Wang, Y., Zhao, L., Shih, C.-C., Xu, J., Wang, J., Tong, L., Chen, Z., et al. (2024) · BMC Medical Imaging · doi:10.1186/s12880-024-01247-y · Cited by 25

A unified end-to-end classification model for focal liver lesions

Zhao, L., Liu, S., An, Y., Cai, W., Li, B., Wang, S.-H., Liang, P., Yu, J., & Zhao, J. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105260 · Cited by 6

Deep Feature Extraction Using Channel Self-Attention for Focal Liver Lesion Classification

Lingamaiah, S. C., & Kokil, P. (2025) · IEEE Conference on Information and Communication Technology (CICT) · doi:10.1109/cict67193.2025.11398967