Feature Selection and Transfer Learning for Alzheimer’s Disease Clinical Diagnosis

Zhou, Ke; He, Wenhao; Xu, Yonghui; Xiong, Gangqiang; Cai, Jie

doi:10.3390/app8081372

Cited by 43 publications

(16 citation statements)

References 28 publications

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“…Images’ weights were derived by minimizing the distance (Kullback-Leibler divergence, Bhattacharyya, squared Euclidean, and maximum mean discrepancy) between the PDFs in the source and target domains [ 47 , 48 , 49 , 50 ]. On the other hand, supervised strategies optimized images’ weights and the ML model simultaneously by minimizing the corresponding task-specific loss [ 45 , 51 , 52 , 53 , 54 ]. Notably, supervised and unsupervised strategies can be combined, and approaches can also incorporate extra information unused in the main task.…”

Section: Resultsmentioning

confidence: 99%

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

et al. 2021

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(1) Background: Transfer learning refers to machine learning techniques that focus on acquiring knowledge from related tasks to improve generalization in the tasks of interest. In magnetic resonance imaging (MRI), transfer learning is important for developing strategies that address the variation in MR images from different imaging protocols or scanners. Additionally, transfer learning is beneficial for reutilizing machine learning models that were trained to solve different (but related) tasks to the task of interest. The aim of this review is to identify research directions, gaps in knowledge, applications, and widely used strategies among the transfer learning approaches applied in MR brain imaging; (2) Methods: We performed a systematic literature search for articles that applied transfer learning to MR brain imaging tasks. We screened 433 studies for their relevance, and we categorized and extracted relevant information, including task type, application, availability of labels, and machine learning methods. Furthermore, we closely examined brain MRI-specific transfer learning approaches and other methods that tackled issues relevant to medical imaging, including privacy, unseen target domains, and unlabeled data; (3) Results: We found 129 articles that applied transfer learning to MR brain imaging tasks. The most frequent applications were dementia-related classification tasks and brain tumor segmentation. The majority of articles utilized transfer learning techniques based on convolutional neural networks (CNNs). Only a few approaches utilized clearly brain MRI-specific methodology, and considered privacy issues, unseen target domains, or unlabeled data. We proposed a new categorization to group specific, widely-used approaches such as pretraining and fine-tuning CNNs; (4) Discussion: There is increasing interest in transfer learning for brain MRI. Well-known public datasets have clearly contributed to the popularity of Alzheimer’s diagnostics/prognostics and tumor segmentation as applications. Likewise, the availability of pretrained CNNs has promoted their utilization. Finally, the majority of the surveyed studies did not examine in detail the interpretation of their strategies after applying transfer learning, and did not compare their approach with other transfer learning approaches.

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Section: Resultsmentioning

confidence: 99%

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

et al. 2021

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“…Several studies in the machine learning area have highlighted the importance of feature selection for the improvement of classification performance [ 20 , 21 ]. For example, an effective feature selection method based on computing the chi-square statistical value was introduced in [ 22 ].…”

Section: Background Of the Studymentioning

confidence: 99%

Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia

Herzog

Magoulas

2021

Sensors

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Early identification of degenerative processes in the human brain is considered essential for providing proper care and treatment. This may involve detecting structural and functional cerebral changes such as changes in the degree of asymmetry between the left and right hemispheres. Changes can be detected by computational algorithms and used for the early diagnosis of dementia and its stages (amnestic early mild cognitive impairment (EMCI), Alzheimer’s Disease (AD)), and can help to monitor the progress of the disease. In this vein, the paper proposes a data processing pipeline that can be implemented on commodity hardware. It uses features of brain asymmetries, extracted from MRI of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, for the analysis of structural changes, and machine learning classification of the pathology. The experiments provide promising results, distinguishing between subjects with normal cognition (NC) and patients with early or progressive dementia. Supervised machine learning algorithms and convolutional neural networks tested are reaching an accuracy of 92.5% and 75.0% for NC vs. EMCI, and 93.0% and 90.5% for NC vs. AD, respectively. The proposed pipeline offers a promising low-cost alternative for the classification of dementia and can be potentially useful to other brain degenerative disorders that are accompanied by changes in the brain asymmetries.

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“…The capability and usefulness of the SVM classification method to discriminate AD patients from healthy ones was demonstrated in other studies [27,28,68]. However, it must be stressed that these works made use of structural brain features only, i.e., morphological information derived from MRI [68]. Even when a reduced number of feature is employed, this method should be regarded as a partial and limited approach to the development of a reliable diagnostic tool.…”

Section: Support Vector Machine Classifiermentioning

confidence: 99%

Combined Use of MRI, fMRIand Cognitive Data for Alzheimer’s Disease: Preliminary Results

et al. 2019

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MRI can favor clinical diagnosis providing morphological and functional information of several neurological disorders. This paper deals with the problem of exploiting both data, in a combined way, to develop a tool able to support clinicians in the study and diagnosis of Alzheimer’s Disease (AD). In this work, 69 subjects from the ADNI open database, 33 AD patients and 36 healthy controls, were analyzed. The possible existence of a relationship between brain structure modifications and altered functions between patients and healthy controls was investigated performing a correlation analysis on brain volume, calculated from the MRI image, the clustering coefficient, derived from fRMI acquisitions, and the Mini Mental Score Examination (MMSE). A statistically-significant correlation was found only in four ROIs after Bonferroni’s correction. The correlation analysis alone was still not sufficient to provide a reliable and powerful clinical tool in AD diagnosis however. Therefore, a machine learning strategy was studied by training a set of support vector machine classifiers comparing different features. The use of a unimodal approach led to unsatisfactory results, whereas the multimodal approach, i.e., the synergistic combination of MRI, fMRI, and MMSE features, resulted in an accuracy of 95.65%, a specificity of 97.22%, and a sensibility of 93.93%.

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Feature Selection and Transfer Learning for Alzheimer’s Disease Clinical Diagnosis

Cited by 43 publications

References 28 publications

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia

Combined Use of MRI, fMRIand Cognitive Data for Alzheimer’s Disease: Preliminary Results

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