Lung cancer cell identification based on artificial neural network ensembles

By Zhi-Hua Zhou, Yuan Jiang, Yubin Yang, Shifu Chen. Artif. Intell. Medicine, 2002

394 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/71a7235b7236232942f96cddffb0b4f633a2e35a

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In Artificial Intelligence

In Molecular Biology

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Medical image analysis with artificial neural networks (2010, methodology): “[9] proposed an automatic pathological diagnosis procedure named Neural Ensemble-based Detection (NED) that utilizes an ANN ensemble to identify lung cancer cells in the specimen images of needle biopsies obtained from the bodies of the subjects to be diagnosed.”
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (2015, methodology): “Among dictionary-based methods, SJTAD performs well in standard CINE MR, but under-performs in CP-BOLD MR.”
Exploring the extremes of sequence/structure space with ensemble fold recognition in the program Phyre (2008, background): “25–27 As a result, various heuristics must be explored.”
Automated trading with performance weighted random forests and seasonality (2014, background): “Ensembles of expert systems have already been successfully applied in many areas such as face recognition (Gutta and Wechsler, 1996), character recognition (Mao, 1998) and medical diagnosis (Zhou et al., 2002) among others.”
Could Machine Learning Break the Convection Parameterization Deadlock? (2018, background): “Applications have included self-driving cars, society games (chess and go) [ Silver et al. , 2016], speech recognition [ Hinton et al. , 2012], object recognition and detection, medical detection of cancers [ Khan et al. , 2001; Zhou et al. , 2002; Karabatak and Ince , 2009], and genomics.”
Extracting symbolic rules from trained neural network ensembles (2003, neutral): “…has great potential, many researchers run into this area and this technology has already been successfully applied to many domains such as optical character recognition [17,26], face recognition [14,18], sci-entiﬁc image analysis [6], medical diagnosis [48], seismic signals classiﬁcation [39], etc.”
The responsibility gap: Ascribing responsibility for the actions of learning automata (2004, neutral): “Zhou et al. present a system for the automatic diagnosis of lung cancer. The system learns to identify cancer cells on microscope images of specimens of needle biopsies obtained from the bodies of the persons to be diagnosed. Systems like that are supposed to be used where an experienced senior path”
A privacy-preserving algorithm for distributed training of neural network ensembles (2012, methodology): “Since neural network ensemble can improve the generalization ability of the learning model significantly, it has been widely used in machine learning and applied successfully to many other areas such as medical diagnosis [13, 14] and financial analysis [11, 12].”
Data Mining in Healthcare and Biomedicine: A Survey of the Literature (2012, methodology): “For example, Zhou et al. used an ensemble approach to identify lung cancer cells for early stage lung cancer diagnosis [ 64 ].Santos-Garcia, likewise, predicted postoperative cardio-respiratory morbidity after pulmonary resection for non-small cell lung cancer using an ensemble approach [65].”
Histology image analysis for carcinoma detection and grading (2012, methodology): “Different schemes are used for accurate performance evaluation such as leave-one-out [137] or more general k-fold cross validation [159].”
Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples (2007, methodology): “Achine learning techniques have been successfully applied to computer-aided diagnosis (CAD) systems [20] [35] [42].”
Use of Machine-Learning Approaches to Predict Clinical Deterioration in Critically Ill Patients: A Systematic Review (2017, background): “ANNs are computational models inspired by the human brain [33].”
An Introduction to Boosting and Leveraging (2002, methodology): “Some further applications to Pose Invariant Face Recognition [94], Lung Cancer Cell Identification [200] and Volatility Estimation for Financial Time Series [7] have also been developed.”
Machine Learning and Radiology (2012, methodology): “Exemplar applications of ensemble learning in medicine include lung cancer cell identification based on ANN ensembles (Zhou et al., 2002), colonic polyp detection using SVM ensembles (Jerebko et al., 2005), and automated classification of lung bronchovascular anatomy in CT using AdaBoost (Ochs et…”