Transfer Learning for Alzheimer's Disease Detection on MRI Images

Ghahnavieh, Amir Ebrahimi; Luo, Siwei; Chiong, Raymond

doi:10.1109/iciaict.2019.8784845

Cited by 75 publications

(28 citation statements)

References 26 publications

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“…The second most popular strategy to apply transfer learning was fine-tuning certain parameters in a pretrained CNN [ 34 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 ]. The remaining approaches first optimized a feature extractor (typically a CNN or a SVM), and then trained a separated model (SVMs [ 30 , 45 , 147 , 148 , 149 ], long short-term memory networks [ 150 , 151 ], clustering methods [ 148 , 152 ], random forests [ 70 , 153 ], multilayer perceptrons [ 154 ], logistic regression [ 148 ], elastic net [ 155 ], CNNs [ 156 ]). Additionally, Yang et al [ 157 ] ensembled CNNs and fine-tuned their individual contribution.…”

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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“…We consider three different popular open source deep learning models with CNN structures for the AD classification task. These models include VGG16 [12], ResNet50 [13], DenseNet121 [14] that were originally created for the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) [24] and were shown to perform well for AD classification [18,21]. We briefly summarize the main characteristics of these models below.…”

Section: Prediction Modelsmentioning

confidence: 99%

“…In comparison, the VGG16 model that is trained from scratch was only able to achieve 74.12% accuracy. Transfer learning performed on MRI scans from the ADNI dataset using architectures such as VGGNet and ResNet [13] is also demonstrated to achieve better performance when compared to 3D-CNN model trained from scratch [21].…”

Section: Introductionmentioning

confidence: 96%

Using ProtoPNet for Interpretable Alzheimer’s Disease Classification

Mohammadjafari

Çevik

Thanabalasingam

et al. 2021

Proceedings of the Canadian Conference on Artificial Intelligence

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Early detection of Alzheimer's disease (AD) is significant for identifying of better treatment plans for the patients as the AD is not curable. On the other hand, lack of interpretability for the high performing prediction models might prevent incorporation of such models in clinical usage for AD detection. Accordingly, it is important to develop highly interpretable models which can create trust towards the prediction models by showing the factors that contribute to the models' decisions. In this paper, we use ProtoPNet architecture in combination with popular pretrained deep learning models to add interpretability to the AD classifications over MRI scans from ADNI and OASIS datasets. We find that the ProtoPNet model with DenseNet121 architecture can reach 90 percent accuracy while providing explanatory illustrations of the model's reasonings for the generated predictions. We also note that, in most cases, the performances of the ProtoPNet models are slightly inferior to their black-box counterparts, however, their ability to provide reasoning and transparency in the prediction generation process can contribute to higher adoption of the prediction models in clinical practice.

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“…The second most popular strategy to apply transfer learning was fine-tuning certain parameters in a pretrained CNN [32,[125][126][127][128][129][130][131][132][133][134][135][136][137][138][139][140][141][142][143][144]. The remaining approaches first optimized a feature extractor (typically a CNN or a SVM), and then trained a separated model (SVMs [28,43,[145][146][147], long short-term memory networks [148,149], clustering methods [146,150], random forests [68,151], multilayer perceptrons [152], logistic regression [146], elastic net [153], CNNs [154]). Additionally, Yang et al [155] ensembled CNNs and fine-tuned their individual contribution.…”

Section: Parameter-based Approachesmentioning

confidence: 99%

Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Review

Valverde,

Imani,

Abdollahzadeh

et al. 2021

Preprint

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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 to re-utilize 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 of knowledge, applications, and widely used strategies among the transfer learning approaches applied in MR brain imaging. 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. 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. A majority of articles utilized transfer learning techniques based on convolutional neural networks (CNNs). Only few approaches utilized clearly brain MRI-specific methodology, considered privacy issues, unseen target domains or unlabeled data. We proposed a new categorization to group specific, widely-used approaches such as pre-training and fine-tuning CNNs. Discussion: There is an increasing interest in transfer learning within 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.

show abstract

Transfer Learning for Alzheimer's Disease Detection on MRI Images

Cited by 75 publications

References 26 publications

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

Using ProtoPNet for Interpretable Alzheimer’s Disease Classification

Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Review

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