An Unsupervised Machine Learning Approach for Medical Image Analysis

[1]  Huan Liu,et al.  Feature Selection for Clustering: A Review , 2018, Data Clustering: Algorithms and Applications.

[2]  Oscar Camacho Nieto,et al.  A machine learning approach to medical image classification: Detecting age-related macular degeneration in fundus images , 2017, Comput. Electr. Eng..

[3]  Roberto Pozzi Mucelli,et al.  Image quality criteria for computed tomography , 1989 .

[4]  Mauro Mazzei,et al.  Spatial Statistical Models for the Evaluation of the Landscape , 2013, ICCSA.

[5]  G. Victo Sudha George,et al.  Review on Feature Selection Techniques and the Impact of SVM for Cancer Classification using Gene Expression Profile , 2011, ArXiv.

[6]  J. M. Ollinger,et al.  Positron Emission Tomography , 2018, Handbook of Small Animal Imaging.

[7]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[8]  Thrasyvoulos N. Pappas An adaptive clustering algorithm for image segmentation , 1992, IEEE Trans. Signal Process..

[9]  Varvara Nika Machine Learning and its Application in Automatic Change Detection in Medical Images , 2014 .

[10]  G. Sagerer,et al.  Methods for automatic microarray image segmentation , 2003, IEEE Transactions on NanoBioscience.

[11]  Yong Zhou,et al.  Joint graph regularized extreme learning machine for multi-label image classification , 2018, J. Comput. Methods Sci. Eng..

[12]  Mauro Mazzei An Unsupervised Machine Learning Approach in Remote Sensing Data , 2019, ICCSA.

[13]  Perikumar Javia,et al.  Machine Learning Algorithms for Classification of Microcirculation Images from Septic and Non-septic Patients , 2018, 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA).

[14]  Adrian D. C. Chan,et al.  A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses , 2005, IEEE Transactions on Biomedical Engineering.

[15]  J. Ambrose,et al.  Computerized transverse axial scanning (tomography): Part 2. Clinical application* , 1973 .

[16]  R A Brooks,et al.  Theory of image reconstruction in computed tomography. , 1975, Radiology.

[17]  Nizar Bouguila,et al.  Practical Bayesian estimation of a finite beta mixture through gibbs sampling and its applications , 2006, Stat. Comput..

[18]  Pat Langley,et al.  Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..