QC-Automator: Deep Learning-Based Automated Quality Control for Diffusion MR Images

Samani, Zahra Riahi; Alappatt, Jacob A.; Parker, Drew; Ismail, Abdol Aziz Ould; Verma, Ragini

doi:10.3389/fnins.2019.01456

Cited by 26 publications

(18 citation statements)

References 38 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 ]).…”

Section: Resultsmentioning

confidence: 99%

“…Fine-tuning all parameters (prior-sharing) and fine-tuning certain parameters (parameter-sharing) were widely used methods, although in the latter case we rarely found justifications for choosing which parameters to fine-tune. Since the first layers of CNNs capture low-level information, such as borders and corners, various studies [ 34 , 134 , 135 , 137 , 145 ] have considered that those parameters can be shared across domains. Besides, as adapting pretrained CNNs to the target domain data requires, at least, replacing the last layer of these models, researchers have likely turn fine-tuning only this randomly-initialized layer into common practice, although we found no empirical studies that supported such practice.…”

Section: Discussionmentioning

confidence: 99%

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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%

Section: Discussionmentioning

confidence: 99%

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

et al. 2021

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“…Each classifier must detect one sub-concept of the current node from the others. One could use a stronger classification approach to have a better summarization method [49].…”

Section: Hierarchical Classificationmentioning

confidence: 99%

Image Collection Summarization Method Based on Semantic Hierarchies

Samani

Moghaddam

2020

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The size of internet image collections is increasing drastically. As a result, new techniques are required to facilitate users in browsing, navigation, and summarization of these large volume collections. Image collection summarization methods present users with a set of exemplar images as the most representative ones from the initial image collection. In this study, an image collection summarization technique was introduced according to semantic hierarchies among them. In the proposed approach, images were mapped to the nodes of a pre-defined domain ontology. In this way, a semantic hierarchical classifier was used, which finally mapped images to different nodes of the ontology. We made a compromise between the degree of freedom of the classifier and the goodness of the summarization method. The summarization was done using a group of high-level features that provided a semantic measurement of information in images. Experimental outcomes indicated that the introduced image collection summarization method outperformed the recent techniques for the summarization of image collections.

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“…Deep learning tools, especially convolutional neural networks (CNNs), are particularly effective in elucidating local patterns 20 , 21 . They learn the underlying features of importance as well as the discrimination algorithm.…”

Section: Introductionmentioning

confidence: 99%

Distinct tumor signatures using deep learning-based characterization of the peritumoral microenvironment in glioblastomas and brain metastases

Samani

Parker

Hodges

et al. 2021

Sci Rep

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Tumor types are classically distinguished based on biopsies of the tumor itself, as well as a radiological interpretation using diverse MRI modalities. In the current study, the overarching goal is to demonstrate that primary (glioblastomas) and secondary (brain metastases) malignancies can be differentiated based on the microstructure of the peritumoral region. This is achieved by exploiting the extracellular water differences between vasogenic edema and infiltrative tissue and training a convolutional neural network (CNN) on the Diffusion Tensor Imaging (DTI)-derived free water volume fraction. We obtained 85% accuracy in discriminating extracellular water differences between local patches in the peritumoral area of 66 glioblastomas and 40 metastatic patients in a cross-validation setting. On an independent test cohort consisting of 20 glioblastomas and 10 metastases, we got 93% accuracy in discriminating metastases from glioblastomas using majority voting on patches. This level of accuracy surpasses CNNs trained on other conventional DTI-based measures such as fractional anisotropy (FA) and mean diffusivity (MD), that have been used in other studies. Additionally, the CNN captures the peritumoral heterogeneity better than conventional texture features, including Gabor and radiomic features. Our results demonstrate that the extracellular water content of the peritumoral tissue, as captured by the free water volume fraction, is best able to characterize the differences between infiltrative and vasogenic peritumoral regions, paving the way for its use in classifying and benchmarking peritumoral tissue with varying degrees of infiltration.

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QC-Automator: Deep Learning-Based Automated Quality Control for Diffusion MR Images

Cited by 26 publications

References 38 publications

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

Image Collection Summarization Method Based on Semantic Hierarchies

Distinct tumor signatures using deep learning-based characterization of the peritumoral microenvironment in glioblastomas and brain metastases

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