Computer-Assisted Decision Support System in Pulmonary Cancer detection and stage classification on CT images
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When applied as a second reader for CT and PET imaging, AI can reduce radiologists' workload while improving the detection of suspicious nodules.
“When applied as a second reader for CT and PET imaging, AI can reduce radiologists’ workload while improving the detection of suspicious nodules.”
From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [39]
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Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741 · see citing works
The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository
Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., et al. (2013) · Journal of Digital Imaging · doi:10.1007/s10278-013-9622-7 · Cited by 4,599 · see citing works
Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks
Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.222 · Cited by 3,150 · see citing works
Solano, J. P., Gomes, B., & Higginson, I. J. (2006) · Journal of Pain and Symptom Management · doi:10.1016/j.jpainsymman.2005.06.007 · Cited by 1,191 · 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
van Ginneken, B., Setio, A. A. A., Jacobs, C., & Ciompi, F. (2015) · 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2015.7163869 · Cited by 278 · cited 1× · see citing works
“There are two main methods of using CNN, either the researcher can model their own CNN architecture or they can use off-the-shelf CNN for acquiring the features [24].”
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Research works printed but not linked: 22
- N. Bui, M. Zorzi, Health care applications: a solution based on the internet of things, in: Proceedings of the International Symposium on Applied Sciences in Biomedical and Communication Technologies, 2011, pp. 131:1-131:5.
- P. Yang, PRLS-INVES: a general experimental investigation strategy for high ac- curacy and precision in passive RFID location systems, IEEE Internet Things J. 2 (2) (2015) 159-167.
- P. Yang, D. Stankevicius, V. Marozas, Z. Deng, E. Liu, A. Lukosevicius, F. Dong, L. Xu, G. Min, Lifelogging data validation model for internet of things enabled personalized healthcare, IEEE Trans. Syst. Man Cybernet.: Syst. (2016).
- M. Hassanalieragh, A. Page, T. Soyata, G. Sharma, M. Aktas, G. Mateos, B. Kantarci, S. Andreescu, Health monitoring and management using internet-of-things (IoT) sensing with cloud-based processing: opportunities and challenges, in: Proceedings of the IEEE International Conference on Services Computing, 2015, pp. 285-292.
- B. Reeder, A. David, Health at hand: a systematic review of smart watch uses for health and wellness, J. Biomed. Inform. 63 (2016) 269-276.
- P. Yang, W. Wu, M. Moniri, C.C. Chibelushi, Efficient object localization using sparsely distributed passive RFID tags, IEEE Trans. Ind. Electron. 60 (12) (2013) 5914-5924.
- F. Hu, D. Xie, S. Shen, On the application of the Internet of things in the field of medical and health care, in: Proceedings of the IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, 2013, pp. 2053-2058.
- S.M.R. Islam, D. Kwak, M.H. Kabir, M. Hossain, K.S. Kwak, The Internet of things for health care: a comprehensive survey, IEEE Access 3 (2015) 678-708.
- K. Ullah, M.A. Shah, S. Zhang, Effective ways to use Internet of things in the field of medical and smart health care, in: Proceedings of the International Conference on Intelligent Systems Engineering, 2016, pp. 372-379.
- X. Yan, J. Pang, H. Qi, Y. Zhu, C. Bai, X. Geng, M. Liu, D. Terzopoulos, X. Ding, Classification of lung nodule malignancy risk on computed tomography images using convolutional neural network: a comparison between 2D and 3D strategies, in: C.-S. Chen, J. Lu, K.-K. Ma (Eds.), LNCS 10118, 2016, pp. 91-101.
- S. Hussein, R. Gillies, K. Cao, Q. Song, U. Bagci, TumorNet: lung nodule char- acterization using multi-view convolutional neural network with Gaussian process, in: Proceedings of the IEEE International Symposium on Biomedical Imaging, 2017, pp. 1007-1010.
- R. Agarwal, A. Shankhadhar, R.K. Sagar, Detection of lung cancer using content based medical image retrieval, in: Proceedings of the International Conference on Advanced Computing & Communication Technologies, 2015, pp. 48-52.
- J. Long, E. Shelhamer, T. Darrell, Fully convolutional networks for semantic seg- mentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 3431-3440.
- C. Bhatt, N. Dey, A.S. Ashour (Eds.), Internet of Things and Big Data Technologies for Next Generation Healthcare, Studies in Big Data 23, Springer, 2017.
- O. Grove, A.E. Berglund, M.B. Schabath, et al., Data from: quantitative computed tomographic descriptors associate tumor shape complexity and intratumor hetero- geneity with prognosis in lung adenocarcinoma, LungCT-Diagnosis Data, Cancer Imag. Arch. (2015), http://dx.doi.org/10.7937/K9/TCIA.2015.A6V7JIWX. · DOI
- G. Han, X. Liu, F. Han, I.N.T. Santika, Y. Zhao, X. Zhao, C. Zhou, The LISS-a public database of common imaging signs of lung diseases for computer-aided detection and diagnosis research and medical education, IEEE Trans. Biomed. Eng. 62 (2) (2015) 648-656.
- S.G. Armato III, L. Hadjiiski, G.D. Tourassi, K. Drukker, M.L. Giger, F. Li, G. Redmond, K. Farahani, J.S. Kirby, L.P. Clarke, SPIE-AAPM-NCI lung nodule classification challenge dataset, Cancer Imag. Arch. (2015), http://dx.doi.org/10. 7937/K9/TCIA.2015.UZLSU3FL. · DOI
- A.K. Jain, F. Farrokhnia, Unsupervised texture segmentation using Gabor filters, in: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 1990, pp. 14-19.
- A.L. Maas, A.Y. Hannun, A.Y. Ng, Rectifier nonlinearities improve neural network acoustic models, in: Proceedings of the International Conference on Machine Learning, 2013.
- W. Liu, Y. Wen, Z. Yu, M. Yang, Large-margin softmax loss for convolutional neural networks, in: Proceedings of the International Conference on Machine Learning, 2016.
- S. Ioffe, C. Szegedy, Batch normalization: accelerating deep network training by reducing internal covariate shift, in: Proceedings of the International Conference on Machine Learning, 2015.
- L. Boroczky, L. Zhao, K.P. Lee, Feature subset selection for improving the perfor- mance of false positive reduction in lung nodule CAD, IEEE Trans. Inform. Technol. Biomed. 10 (3) (2006) 504-511.
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Sorted by citations
A review of medical artificial intelligence
Liu, R., Rong, Y., & Peng, Z. (2020) · Global Health Journal · doi:10.1016/j.glohj.2020.04.002 · Cited by 63 · see citing works
Petrella, F., & Rizzo, S. (2026) · Cancers · doi:10.3390/cancers18020246 · Cited by 10 · see citing works
- Authors
- Masood, A., Sheng, B., Li, P., Hou, X., Wei, X., Qin, J., & Feng, D.
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2018-03
- Volume
- 79
- Pages
- 117-128
- ISSN
- 1532-0464
- Cited by
- 250
- References
- 6
- Language
- en
- Licence
- Funders
- National Natural Science Foundation of China (61572316, 61671290)
- National High-tech R&D Program of China (863 Program) (2015AA015904)
- Key Program for International S&T Cooperation Project (2016YFE0129500)
- Science and Technology Commission of Shanghai Municipality (16DZ0501100, 17411952600)
- General Program of Cross of Medicine and Engineering of SJTU (YG2015MS19)
- interdisciplinary Program of Shanghai Jiao Tong University (14JCY10)
- Research Grants Council of Hong Kong (28200215)
- The Hong Kong Polytechnic University
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