Machine learning for medical applications

Machine learning has been well applied and recognized as an effective tool to handle a wide range of real situations, including medical applications. In this scenario, it can help to alleviate problems typically suffered by researchers in this field, such as saving time for practitioners and providing unbiased results. This tutorial is concerned with the use of machine learning techniques to solve different medical problems. We provide a survey of recent methods developed or applied to this context, together with a review of novel contributions to the ESANN 2016 special session on Machine learning for medical applications.

[1]  Emilio Soria-Olivas,et al.  Multi-step strategy for mortality assessment in cardiovascular risk patients with imbalanced data , 2016, ESANN.

[2]  Verónica Bolón-Canedo,et al.  Dealing with inter-expert variability in retinopathy of prematurity: A machine learning approach , 2015, Comput. Methods Programs Biomed..

[3]  S. R. Kannan,et al.  Effective FCM noise clustering algorithms in medical images , 2013, Comput. Biol. Medicine.

[4]  Daniel Rueckert,et al.  Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression , 2012, NeuroImage.

[5]  U. Rajendra Acharya,et al.  Automated Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features , 2011, IEEE Transactions on Information Technology in Biomedicine.

[6]  Paul M. Thompson,et al.  Segmentation of High Angular Resolution Diffusion MRI Using Sparse Riemannian Manifold Clustering , 2014, IEEE Transactions on Medical Imaging.

[7]  Oscar Fontenla-Romero,et al.  A fast learning algorithm for high dimensional problems: an application to microarrays , 2016, ESANN.

[8]  nbspSarfaraz Ahmed.  Medical Diagnosis using Neural Networks , 2014 .

[9]  Verónica Bolón-Canedo,et al.  Feature Selection for High-Dimensional Data , 2015, Artificial Intelligence: Foundations, Theory, and Algorithms.

[10]  Max A. Viergever,et al.  elastix: A Toolbox for Intensity-Based Medical Image Registration , 2010, IEEE Transactions on Medical Imaging.

[11]  Beatriz Remeseiro,et al.  A Methodology for Improving Tear Film Lipid Layer Classification , 2014, IEEE Journal of Biomedical and Health Informatics.

[12]  Sahar Bayat,et al.  Improving Case-Based Reasoning Systems by Combining K-Nearest Neighbour Algorithm with Logistic Regression in the Prediction of Patients’ Registration on the Renal Transplant Waiting List , 2013, PloS one.

[13]  Albert Pla,et al.  Bag-of-Steps: predicting lower-limb fracture rehabilitation length , 2017, ESANN.

[14]  Dimitrios I. Fotiadis,et al.  Machine learning applications in cancer prognosis and prediction , 2014, Computational and structural biotechnology journal.

[15]  Timothy F. Cootes,et al.  Fully Automatic Segmentation of the Proximal Femur Using Random Forest Regression Voting , 2013, IEEE Transactions on Medical Imaging.

[16]  Ludwig Kappos,et al.  Quantifying Progression of Multiple Sclerosis via Classification of Depth Videos , 2014, MICCAI.

[17]  Igor Kononenko,et al.  Machine learning for medical diagnosis: history, state of the art and perspective , 2001, Artif. Intell. Medicine.

[18]  Isaac Fernández-Varela,et al.  Automatic detection of EEG arousals , 2016, ESANN.

[19]  Joseph M. Reinhardt,et al.  Splat Feature Classification With Application to Retinal Hemorrhage Detection in Fundus Images , 2013, IEEE Transactions on Medical Imaging.

[20]  Verónica Bolón-Canedo,et al.  Data complexity measures for analyzing the effect of SMOTE over microarrays , 2016, ESANN.

[21]  Beatriz Remeseiro,et al.  Automatic classification of the interferential tear film lipid layer using colour texture analysis , 2013, Comput. Methods Programs Biomed..

[22]  John Doucette,et al.  Adopting electronic medical records in primary care: Lessons learned from health information systems implementation experience in seven countries , 2009, Int. J. Medical Informatics.

[23]  Antonio Criminisi,et al.  Regression forests for efficient anatomy detection and localization in computed tomography scans , 2013, Medical Image Anal..

[24]  Tien Yin Wong,et al.  Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening , 2013, IEEE Transactions on Medical Imaging.

[25]  Brian B. Avants,et al.  Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain , 2008, Medical Image Anal..

[26]  Francisco Herrera,et al.  Data Preprocessing in Data Mining , 2014, Intelligent Systems Reference Library.

[27]  Pierre-Antoine Absil,et al.  Spatiotemporal ICA improves the selection of differentially expressed genes , 2016, ESANN.

[28]  Daniel Rueckert,et al.  Random forest-based similarity measures for multi-modal classification of Alzheimer's disease , 2013, NeuroImage.

[29]  David S. Wishart,et al.  Applications of Machine Learning in Cancer Prediction and Prognosis , 2006, Cancer informatics.

[30]  Aïda Valls,et al.  Assessment of diabetic retinopathy risk with random forests , 2016, ESANN.

[31]  Zhuowen Tu,et al.  Multiple clustered instance learning for histopathology cancer image classification, segmentation and clustering , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[32]  Timothy F. Cootes,et al.  Accurate Bone Segmentation in 2D Radiographs Using Fully Automatic Shape Model Matching Based On Regression-Voting , 2013, MICCAI.

[33]  Sabine Van Huffel,et al.  Initializing nonnegative matrix factorization using the successive projection algorithm for multi-parametric medical image segmentation , 2016, ESANN.

[34]  Peter Szolovits,et al.  Automatic lymphoma classi fi cation with sentence subgraph mining from pathology reports , 2014 .

[35]  Antonio Mosquera González,et al.  On the analysis of feature selection techniques in a conjunctival hyperemia grading framework , 2016, ESANN.

[36]  Gaël Varoquaux,et al.  A supervised clustering approach for fMRI-based inference of brain states , 2011, Pattern Recognit..

[37]  Verónica Bolón-Canedo,et al.  Using a feature selection ensemble on DNA microarray datasets , 2016, ESANN.

[38]  David Dagan Feng,et al.  Feature-Based Image Patch Approximation for Lung Tissue Classification , 2013, IEEE Transactions on Medical Imaging.

[39]  Ricardo Gamelas Sousa,et al.  Stacked denoising autoencoders for the automatic recognition of microglial cells' state , 2016, ESANN.

[40]  Jorge Novo,et al.  Feature definition, analysis and selection for lung nodule classification in chest computerized tomography images , 2016, ESANN.

[41]  Daniel W. Apley,et al.  A design of experiments approach to validation sampling for logistic regression modeling with error-prone medical records , 2016, J. Am. Medical Informatics Assoc..

[42]  Nan Zhang,et al.  Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation , 2011, Comput. Vis. Image Underst..