Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects.
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TLDR
The results presented in this study demonstrate the potential of the suggested approach for early AD diagnosis and an important role of MRI in the MCI-to-AD conversion prediction.About:
This article is published in NeuroImage.The article was published on 2015-01-01 and is currently open access. It has received 549 citations till now. The article focuses on the topics: Cognitive decline & Biomarker (medicine).read more
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Building better biomarkers: brain models in translational neuroimaging
TL;DR: The state of translational neuroimaging is reviewed, an approach to developing brain signatures that can be shared, tested in multiple contexts and applied in clinical settings is outlined and a program of broad exploration followed by increasingly rigorous assessment of generalizability is outlined.
Journal ArticleDOI
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.
TL;DR: There is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders, however, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper.
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Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
TL;DR: In this article, a survey of semi-supervised, multiple instance and transfer learning in medical image segmentation is presented, and connections between these learning scenarios, and opportunities for future research are discussed.
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A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages
TL;DR: The studies reviewed indicate that the classification frameworks formulated on the basis of these features show promise for individualized diagnosis and prediction of clinical progression, and a detailed account of AD classification challenges is provided.
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Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications
TL;DR: The underlying concepts of DL are introduced and studies that have used this approach to classify brain‐based disorders are reviewed, indicating that DL could be a powerful tool in the current search for biomarkers of psychiatric and neurologic disease.
References
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Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Classification and Regression by randomForest
Andy Liaw,Matthew C. Wiener +1 more
TL;DR: random forests are proposed, which add an additional layer of randomness to bagging and are robust against overfitting, and the randomForest package provides an R interface to the Fortran programs by Breiman and Cutler.
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The use of the area under the ROC curve in the evaluation of machine learning algorithms
TL;DR: AUC exhibits a number of desirable properties when compared to overall accuracy: increased sensitivity in Analysis of Variance (ANOVA) tests; a standard error that decreased as both AUC and the number of test samples increased; decision threshold independent; and it is invariant to a priori class probabilities.
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Semi-Supervised Learning
TL;DR: Semi-supervised learning (SSL) as discussed by the authors is the middle ground between supervised learning (in which all training examples are labeled) and unsupervised training (where no label data are given).
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