References
- Samuel A. Some studies in machine learning using the game of checkers. IBM J Res Dev. 1959. https://doi.org/10.1147/rd.33.0210.
- http://www.britannica.com/EBchecked/topic/1116194/machinelearning.
- Kohavi R, Provost F. Glossary of terms. Mach Learn. 1998;30:271-4. https://doi.org/10.1023/A:1017181826899
- Sajid I, Khan UG, Saba T, Rehman A. Computer-assisted brain tumor type discrimination using magnetic resonance imaging features. Biomed Eng Lett. 2018.
- Lahmiri S, Dawson DA, Shmuel A. Performance of machine learning methods in diagnosing Parkinson's disease based on dysphonia measures. Biomed Eng Lett. 2018.
- Mansour RF. Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy. Biomed Eng Lett. 2018.
- Relan D, Relan R. Multiscale self-quotient filtering for an improved unsupervised retinal blood vessels characterisations. Biomed Eng Lett. 2018.
- Billah M, Washeed S. Gastrointestinal polyp detection in endoscopic images using an improved feature extraction method. Biomed Eng Lett. 2018.
- Beritelli F, Capizzi G, Sciuto GL, Napoliy C, Scaglione F. Automatic heart activity diagnosis based on gram polynomials and probabilistic neural networks. Biomed Eng Lett. 2018.
- Wei R, Zhang X, Wang J, Dang X. The research of sleep staging based on single-lead electrocardiogram and deep neural network. Biomed Eng Lett. 2018.
- Dey D, Chaudhuri S, Munshi S. Obstructive sleep apnoea detection using convolutional neural network based deep learning framework. Biomed Eng Lett. 2018.
Cited by
- Identifying substance use risk based on deep neural networks and Instagram social media data vol.44, pp.3, 2019, https://doi.org/10.1038/s41386-018-0247-x
- Generation of PET Attenuation Map for Whole-Body Time-of-Flight 18F-FDG PET/MRI Using a Deep Neural Network Trained with Simultaneously Reconstructed Activity and Attenuation Maps vol.60, pp.8, 2019, https://doi.org/10.2967/jnumed.118.219493
- Deep-ACTINet: End-to-End Deep Learning Architecture for Automatic Sleep-Wake Detection Using Wrist Actigraphy vol.8, pp.12, 2019, https://doi.org/10.3390/electronics8121461
- Evaluation of Transfer Learning with Deep Convolutional Neural Networks for Screening Osteoporosis in Dental Panoramic Radiographs vol.9, pp.2, 2020, https://doi.org/10.3390/jcm9020392
- Advances in Hybrid Fabrication toward Hierarchical Tissue Constructs vol.7, pp.11, 2020, https://doi.org/10.1002/advs.201902953
- Predicting the Risk of Inpatient Hypoglycemia With Machine Learning Using Electronic Health Records vol.43, pp.7, 2018, https://doi.org/10.2337/dc19-1743
- HyCAD-OCT: A Hybrid Computer-Aided Diagnosis of Retinopathy by Optical Coherence Tomography Integrating Machine Learning and Feature Maps Localization vol.10, pp.14, 2018, https://doi.org/10.3390/app10144716
- Deep Learning: High-quality Imaging through Multicore Fiber vol.4, pp.4, 2018, https://doi.org/10.3807/copp.2020.4.4.286
- Evaluation of Transfer Learning with CNN to classify the Jaw Tumors vol.928, pp.None, 2018, https://doi.org/10.1088/1757-899x/928/3/032072
- Whole‐brain functional connectivity correlates of obesity phenotypes vol.41, pp.17, 2018, https://doi.org/10.1002/hbm.25167
- The orbitofrontal cortex functionally links obesity and white matter hyperintensities vol.10, pp.None, 2018, https://doi.org/10.1038/s41598-020-60054-x
- Digitally Fabricated and Naturally Augmented In Vitro Tissues vol.10, pp.2, 2018, https://doi.org/10.1002/adhm.202001253
- Cardiac Disorder Classification by Electrocardiogram Sensing Using Deep Neural Network vol.2021, pp.None, 2018, https://doi.org/10.1155/2021/5512243
- Development and Validation of a Machine Learning Model to Predict Near-Term Risk of Iatrogenic Hypoglycemia in Hospitalized Patients vol.4, pp.1, 2021, https://doi.org/10.1001/jamanetworkopen.2020.30913
- A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics vol.8, pp.None, 2018, https://doi.org/10.3389/fcvm.2021.699145
- Probabilistic feature extraction, dose statistic prediction and dose mimicking for automated radiation therapy treatment planning vol.48, pp.9, 2018, https://doi.org/10.1002/mp.15098
- Predictive model and risk analysis for diabetic retinopathy using machine learning: a retrospective cohort study in China vol.11, pp.11, 2021, https://doi.org/10.1136/bmjopen-2021-050989
- Graph-based complex network features for the auscultation of mitral incompetence vol.74, pp.None, 2018, https://doi.org/10.1016/j.cjph.2021.09.001
- Droplet based microfluidics integrated with machine learning vol.332, pp.p1, 2018, https://doi.org/10.1016/j.sna.2021.113096
- A deep learning algorithm for automated measurement of vertebral body compression from X-ray images vol.11, pp.1, 2021, https://doi.org/10.1038/s41598-021-93017-x