References
[1]. A. Ng, (2004)."Feature selection, L1 vs. L2 regularization, and rotational invariance," in Proceedings of the 21st International Conference on Machine Learning (ICML), 04 Proceedings of the twenty-first international conference on Machine learning, Stanford.
[2]. Y. LeCun, Y. Bengio, and G. Hinton. (2015). "Deep learning," Nature, 521 (7553), 436-444.
[3]. Andre Esteva, Brett Kuprel, Roberto A. Novoa et al. (2017).Dermatologist-level classification of skin cancer with deep neural networks. Nature,542(7639),115-118.
[4]. Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu, et al. (2017). CheXNet: Radiologist-level pneumonia detection on chest x-rays with deep learning.arXiv preprint arXiv: 1711.05225.
[5]. Trevor Hastie, Robert Tibshirani, Jerome Friedman (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer. 33.
[6]. Varun Gulshan, Lily Peng, Marc Coram, Martin C. Stumpe et al. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA,316(22),2402-2410.
[7]. Bishop, C.M. (2006). Pattern Recognition and Machine Learning. Springer. 351.
[8]. Dmitrii Bychkov, Nina Linder, Riku Turkki et al. (2018). Deep learning based tissue analysis predicts outcome in colorectal cancer. Scientific Reports,8(1),1-11.
[9]. Scott M. McKinney, Marc Sieniek, Varun Godbole, et al. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89-94.
[10]. Ryan Poplin, Avinash Varadarajan, Katy Blumer et al. (2018). Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature Biomedical Engineering,2(3),158-164.
Cite this article
Meng,Q. (2024). Application of machine learning in medicine. Applied and Computational Engineering,33,207-212.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. A. Ng, (2004)."Feature selection, L1 vs. L2 regularization, and rotational invariance," in Proceedings of the 21st International Conference on Machine Learning (ICML), 04 Proceedings of the twenty-first international conference on Machine learning, Stanford.
[2]. Y. LeCun, Y. Bengio, and G. Hinton. (2015). "Deep learning," Nature, 521 (7553), 436-444.
[3]. Andre Esteva, Brett Kuprel, Roberto A. Novoa et al. (2017).Dermatologist-level classification of skin cancer with deep neural networks. Nature,542(7639),115-118.
[4]. Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu, et al. (2017). CheXNet: Radiologist-level pneumonia detection on chest x-rays with deep learning.arXiv preprint arXiv: 1711.05225.
[5]. Trevor Hastie, Robert Tibshirani, Jerome Friedman (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer. 33.
[6]. Varun Gulshan, Lily Peng, Marc Coram, Martin C. Stumpe et al. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA,316(22),2402-2410.
[7]. Bishop, C.M. (2006). Pattern Recognition and Machine Learning. Springer. 351.
[8]. Dmitrii Bychkov, Nina Linder, Riku Turkki et al. (2018). Deep learning based tissue analysis predicts outcome in colorectal cancer. Scientific Reports,8(1),1-11.
[9]. Scott M. McKinney, Marc Sieniek, Varun Godbole, et al. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89-94.
[10]. Ryan Poplin, Avinash Varadarajan, Katy Blumer et al. (2018). Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature Biomedical Engineering,2(3),158-164.