Deep fMRI: AN end-to-end deep network for classification of fMRI data

Riaz, Arshad; Asad, Muhammad; Arif, Sheeraz; Alonso, Eduardo; Dima, Danai; Corr, Philip J.; Slabaugh, Greg

doi:10.1109/isbi.2018.8363838

Cited by 46 publications

(48 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Subsequently, we further calculated the overall accuracy for all sites and compared our method with several state-of-the-art methods. As listed in Table 2 , our method outperformed the ADHD-200 competition teams [ 39 ], as well as the previous state-of-the-art deep learning model including FCNet [ 24 ], 3D-CNN [ 26 ], and DeepFMRI [ 25 ], achieving a 68.6% classification accuracy.…”

Section: Resultsmentioning

confidence: 71%

“…Furthermore, we calculated the average accuracy to evaluate the performance of the model. The highest mean leave-one-site-out accuracy achieved 68.6% by using the SC-CNN-attention model ( Table 2 ), which surpassed previous state-of-the-art deep learning methods [ 24 , 25 , 40 ] using the large and multi-site sample (less five sites).…”

Section: Discussionmentioning

confidence: 78%

“…In general, aggregated heterogeneity datasets bring great challenges to develop suitable classifiers for psychiatric illnesses [ 17 ]. Our current work used five different sites’ dataset ( n = 1019) that were larger than previous studies [ 25 ]. These datasets had no prior coordination, which was a real clinical application setting.…”

Section: Discussionmentioning

confidence: 99%

“…For instance, Riaz et al [ 24 ] proposed a CNN-based deep learning structure, FCNet, to extract FC features from raw resting-state fMRI signals, and they found that the FCNet has superior discriminative power on the ADHD-200 dataset (the highest classification of 62.7% on the Peking dataset). Thereafter, Riaz et al [ 25 ] further proposed an end-to-end deep model to learn FC features automatically and used a classification network to identify ADHD, which achieved an average accuracy of 67.9% by using three different ADHD-200 datasets. Zou [ 26 ] developed a three-dimensional CNN (3D-CNN) model to learn the local spatial patterns of ADHD from multi-model MRI features, and their proposed single modality 3D CNN architecture with the fractional amplitude of low-frequency fluctuations (fALFF) feature also achieved a mean accuracy of 66.04% on the ADHD-200 dataset.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Separated Channel Attention Convolutional Neural Network (SC-CNN-Attention) to Identify ADHD in Multi-Site Rs-fMRI Dataset

Zhang

et al. 2020

Entropy

View full text Add to dashboard Cite

The accurate identification of an attention deficit hyperactivity disorder (ADHD) subject has remained a challenge for both neuroscience research and clinical diagnosis. Unfortunately, the traditional methods concerning the classification model and feature extraction usually depend on the single-channel model and static measurements (i.e., functional connectivity, FC) in the small, homogenous single-site dataset, which is limited and may cause the loss of intrinsic information in functional MRI (fMRI). In this study, we proposed a new two-stage network structure by combing a separated channel convolutional neural network (SC-CNN) with an attention-based network (SC-CNN-attention) to discriminate ADHD and healthy controls on a large-scale multi-site database (5 sites and n = 1019). To utilize both intrinsic temporal feature and the interactions of temporal dependent in whole-brain resting-state fMRI, in the first stage of our proposed network structure, a SC- CNN is used to learn the temporal feature of each brain region, and an attention network in the second stage is adopted to capture temporal dependent features among regions and extract fusion features. Using a “leave-one-site-out” cross-validation framework, our proposed method obtained a mean classification accuracy of 68.6% on five different sites, which is higher than those reported in previous studies. The classification results demonstrate that our proposed network is robust to data variants and is also replicated across sites. The combination of the SC-CNN with the attention network is powerful to capture the intrinsic fMRI information to discriminate ADHD across multi-site resting-state fMRI data.

show abstract

Section: Resultsmentioning

confidence: 71%

Section: Discussionmentioning

confidence: 78%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Separated Channel Attention Convolutional Neural Network (SC-CNN-Attention) to Identify ADHD in Multi-Site Rs-fMRI Dataset

Zhang

et al. 2020

Entropy

View full text Add to dashboard Cite

show abstract

“…To account for temporal information, they compute the mean and standard deviation for all voxels and use those 3D volumes as input to the network. Riaz et al 28 did use an end to end deep network for fMRI data classification named DeepFMRI, but the complexity of the network makes it difficult in practice for clinical application. The entire network has three main parts.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal feature extraction and classification of Alzheimer’s disease using deep learning 3D-CNN for fMRI data

et al. 2020

View full text Add to dashboard Cite

Purpose: Through the last three decades, functional magnetic resonance imaging (fMRI) has provided immense quantities of information about the dynamics of the brain, functional brain mapping, and resting-state brain networks. Despite providing such rich functional information, fMRI is still not a commonly used clinical technique due to inaccuracy involved in analysis of extremely noisy data. However, ongoing developments in deep learning techniques suggest potential improvements and better performance in many different domains. Our main purpose is to utilize the potentials of deep learning techniques for fMRI data for clinical use. Approach: We present one such synergy of fMRI and deep learning, where we apply a simplified yet accurate method using a modified 3D convolutional neural networks (CNN) to resting-state fMRI data for feature extraction and classification of Alzheimer's disease (AD). The CNN is designed in such a way that it uses the fMRI data with much less preprocessing, preserving both spatial and temporal information. Results: Once trained, the network is successfully able to classify between fMRI data from healthy controls and AD subjects, including subjects in the mild cognitive impairment (MCI) stage. We have also extracted spatiotemporal features useful for classification. Conclusion: This CNN can detect and differentiate between the earlier and later stages of MCI and AD and hence, it may have potential clinical applications in both early detection and better diagnosis of Alzheimer's disease.

show abstract

Deep Learning‐Based Single‐Cell Optical Image Studies

Sun

Tárnok

2020

Cytometry Pt A

View full text Add to dashboard Cite

Optical imaging technology that has the advantages of high sensitivity and cost‐effectiveness greatly promotes the progress of nondestructive single‐cell studies. Complex cellular image analysis tasks such as three‐dimensional reconstruction call for machine‐learning technology in cell optical image research. With the rapid developments of high‐throughput imaging flow cytometry, big data cell optical images are always obtained that may require machine learning for data analysis. In recent years, deep learning has been prevalent in the field of machine learning for large‐scale image processing and analysis, which brings a new dawn for single‐cell optical image studies with an explosive growth of data availability. Popular deep learning techniques offer new ideas for multimodal and multitask single‐cell optical image research. This article provides an overview of the basic knowledge of deep learning and its applications in single‐cell optical image studies. We explore the feasibility of applying deep learning techniques to single‐cell optical image analysis, where popular techniques such as transfer learning, multimodal learning, multitask learning, and end‐to‐end learning have been reviewed. Image preprocessing and deep learning model training methods are then summarized. Applications based on deep learning techniques in the field of single‐cell optical image studies are reviewed, which include image segmentation, super‐resolution image reconstruction, cell tracking, cell counting, cross‐modal image reconstruction, and design and control of cell imaging systems. In addition, deep learning in popular single‐cell optical imaging techniques such as label‐free cell optical imaging, high‐content screening, and high‐throughput optical imaging cytometry are also mentioned. Finally, the perspectives of deep learning technology for single‐cell optical image analysis are discussed. © 2020 International Society for Advancement of Cytometry

show abstract

Deep fMRI: AN end-to-end deep network for classification of fMRI data

Cited by 46 publications

References 10 publications

Separated Channel Attention Convolutional Neural Network (SC-CNN-Attention) to Identify ADHD in Multi-Site Rs-fMRI Dataset

Separated Channel Attention Convolutional Neural Network (SC-CNN-Attention) to Identify ADHD in Multi-Site Rs-fMRI Dataset

Spatiotemporal feature extraction and classification of Alzheimer’s disease using deep learning 3D-CNN for fMRI data

Deep Learning‐Based Single‐Cell Optical Image Studies

Contact Info

Product

Resources

About