• Corpus ID: 204008972

NEURO-DRAM: a 3D recurrent visual attention model for interpretable neuroimaging classification

@article{Wood2019NEURODRAMA3,
  title={NEURO-DRAM: a 3D recurrent visual attention model for interpretable neuroimaging classification},
  author={David A Wood and James H. Cole and Thomas C. Booth},
  journal={ArXiv},
  year={2019},
  volume={abs/1910.04721}
}
Deep learning is attracting significant interest in the neuroimaging community as a means to diagnose psychiatric and neurological disorders from structural magnetic resonance images. However, there is a tendency amongst researchers to adopt architectures optimized for traditional computer vision tasks, rather than design networks customized for neuroimaging data. We address this by introducing NEURO-DRAM, a 3D recurrent visual attention model tailored for neuroimaging classification. The model… 
View PDF on arXiv
7 Citations
Highly Influential Citations
2
Background Citations
3
Methods Citations
2

Figures and Tables from this paper

7 Citations

Re-transfer learning and multi-modal learning assisted early diagnosis of Alzheimer’s disease

A fine-grained brain image classification approach for diagnosing Alzheimer’s disease, with re-transfer learning and multi-modal learning theories is proposed, which could help doctors diagnose Alzheimer's disease more timely and accurately.

Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research

Interpretability of Machine Learning Methods Applied to Neuroimaging

The most common interpretability methods and metrics developed to assess their reliability, as well as their applications and benchmarks in the neuroimaging context are presented, including SHAP, SHapley Additive exPlanations.

Diagnosis of Alzheimer's disease based on regional attention with sMRI gray matter slices

Preclinical Stage Alzheimer's Disease Detection Using Magnetic Resonance Image Scans

This paper proposes two attention model networks for detecting Alzheimer's disease from MRI images to help early detection efforts at the preclinical stage and compares the performance of these two attention network models with a baseline model.

Diagnosis of Alzheimer ’ s disease based on regional attention with sMRI gray matter slices Journal of Neuroscience Methods

A deep learning framework based on sMRI gray matter slice based on the regional attention with GM slice has a 1% (cid:0) 8% improvement in accuracy compared with several state-of-the-art methods for AD diagnosis.

Towards Greater Neuroimaging Classification Transparency via the Integration of Explainability Methods and Confidence Estimation Approaches

The field of neuroimaging has increasingly sought to develop artificial intelligence-based models for neurological and neuropsychiatric disorder automated diagnosis and clinical decision support.…

References

SHOWING 1-10 OF 53 REFERENCES

Natural Image Bases to Represent Neuroimaging Data

This paper used a sparse autoencoder to learn a set of bases from natural images and then applied convolution to extract features from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and achieved high classification performance, which is competitive with or better than other approaches.

Alzheimer's Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network

To predict the Alzheimer's disease with a deep 3D convolutional neural network (3D-CNN), which can learn generic features capturing AD biomarkers and adapt to different domain datasets, which has been validated on the CADDementia dataset.

Multimodal learning using convolution neural network and Sparse Autoencoder

The proposed deep learning method on fusion multimodalities can achieve a classification accuracy of 90% between AD patients and healthy controls, demonstrating the improvement than using only one modality.

Classification of brain MRI with big data and deep 3D convolutional neural networks

The largest dataset ever used for training a deep 3D convolutional neural network to classify brain images as healthy (HC), mild cognitive impairment (MCI) or Alzheimers disease (AD) is assembled, which is almost 9x the size of the previously largest data set.

Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer's Disease Classification

It is concluded that LRP might have a high potential to assist clinicians in explaining neural network decisions for diagnosing AD (and potentially other diseases) based on structural MRI data.

Classification of MR brain images by combination of multi-CNNs for AD diagnosis

This paper proposes to construct multiple deep 3D convolutional neural networks (3D-CNNs) to learn the various features from local brain images which are combined to make the final classification for AD diagnosis.

Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease

This framework uses a zero-masking strategy for data fusion to extract complementary information from multiple data modalities to aid the diagnosis of AD and has the potential to require less labeled data.

Deep Learning in Alzheimer's Disease: Diagnostic Classification using Neuroimaging Data

Deep learning approaches without preprocessing neuroim imaging data for feature selection, a major bottleneck of traditional machining learning in high-dimensional data, continue to improve their performance and to show great promise in the diagnostic classification of AD using multimodal neuroimaging data.

Deep Learning Based Imaging Data Completion for Improved Brain Disease Diagnosis

This work proposed a deep learning based framework for estimating multi-modality imaging data in the form of convolutional neural networks, where the input and output are two volumetric modalities.

Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis

...