Automatic Recognition of Mild Cognitive Impairment and Alzheimers Disease Using Ensemble based 3D Densely Connected Convolutional Networks

Wang, Shuqiang; Wang, Hongfei; Shen, Yanyan; Wang, Xiangyu

doi:10.1109/icmla.2018.00083

Cited by 49 publications

(14 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides 2D CNN architectures, 3D convolutional layers enabled scientists to incorporate the volumetric data into the training process, and such approaches produced promising predictions using structural MRI data [55]- [57]. The 3D models used the signal intensity at the voxel level and applied the convolution operator to 3D filters and previous-layer feature maps.…”

Section: Related-workmentioning

confidence: 99%

OViTAD: Optimized Vision Transformer to Predict Various Stages of Alzheimer's Disease Using Resting-State fMRI and Structural MRI Data

Sarraf¹,

Sarraf

DeSouza

et al. 2021

Preprint

View full text Add to dashboard Cite

Advances in applied machine learning techniques to neuroimaging have encouraged scientists to implement models to early diagnose brain disorders such as Alzheimer’s Disease. Predicting various stages of Alzheimer’s disease is challenging; however, existing deep learning complex techniques could perform such a prediction. Therefore, using novel architectures with less complexity but efficient pattern extraction capabilities such as transformers has been of interest to neuroscientists. This study introduced an optimized vision transformer architecture to predict the aging effect in healthy adults (>75 years), mild cognitive impairment, and Alzheimer’s’ brains within the same age group using resting-state functional and anatomical magnetic resonance imaging data. Our optimized architecture known as OViTAD, which is currently the sole vision transformer-based end-to-end pipeline, outperformed the existing transformer models and most state-of-the-art solutions with F1-scores of 97%±0.0 and 99.55%±0.39 achieved from the testing sets for the two modalities in the triple-class prediction experiments where the number of trainable parameters decreased by 30% compared to a vanilla transformer. To ensure the robustness and reproducibility of our optimized vision transformer, we repeated the modeling process three times for all the experiments and reported the averaged evaluation metrics. Furthermore, we implemented a visualization technique to illustrate the effect of global attention on brain images. Also, we exhaustively implemented models to explore the impact of combining healthy brains with two other groups in the two modalities. This study could open a new avenue of adopting and optimizing vision transformers for neuroimaging applications, especially for Alzheimer’s Disease prediction.

show abstract

Section: Related-workmentioning

confidence: 99%

OViTAD: Optimized Vision Transformer to Predict Various Stages of Alzheimer's Disease Using Resting-State fMRI and Structural MRI Data

Sarraf¹,

Sarraf

DeSouza

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Recently, deep learning technology has been popularized in medical image processing, and has been applied in maturity recognition [5], [6], disease analysis [7], cross-modal data supplement [8] and other fields. Many deep learning reconstruction models, such as Generative adversarial networks (GANs) and auto encoders (AEs), are widely used in reconstructing 2D images.…”

Section: Related Workmentioning

confidence: 99%

3D Brain Reconstruction by Hierarchical Shape-Perception Network from a Single Incomplete Image

Hu¹,

Lei²,

Liu

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

3D shape reconstruction is essential in the navigation of minimally-invasive and auto robot-guided surgeries whose operating environments are indirect and narrow, and there have been some works that focused on reconstructing the 3D shape of the surgical organ through limited 2D information available. However, the lack and incompleteness of such information caused by intraoperative emergencies (such as bleeding) and risk control conditions have not been considered. In this paper, a novel hierarchical shape-perception network (HSPN) is proposed to reconstruct the 3D point clouds (PCs) of specific brains from one single incomplete image with low latency. A tree-structured predictor and several hierarchical attention pipelines are constructed to generate point clouds that accurately describe the incomplete images and then complete these point clouds with high quality. Meanwhile, attention gate blocks (AGBs) are designed to efficiently aggregate geometric local features of incomplete PCs transmitted by hierarchical attention pipelines and internal features of reconstructing point clouds. With the proposed HSPN, 3D shape perception and completion can be achieved spontaneously. Comprehensive results measured by Chamfer distance and PC-to-PC error demonstrate that the performance of the proposed HSPN outperforms other competitive methods in terms of qualitative displays, quantitative experiment, and classification evaluation.

show abstract

“…However, the details regarding CV methodology and classification decisions are not presented in this study. Wang et al [33] proposed an ensemble of 3D densely connected convolutional networks (3D-DenseNets) for three-class AD, MCI, and HC diagnosis. In their model, MRI scans of the same patients that are over three years apart are employed as different samples, incorporating information of test data into the learning process.…”

Section: Related Workmentioning

confidence: 99%

3D Convolutional Neural Networks for Diagnosis of Alzheimer's Disease via Structural MRI

Yağış

Citi

Diciotti

et al. 2020

2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)

View full text Add to dashboard Cite

Alzheimer's Disease (AD) is a widespread neurodegenerative disease caused by structural changes in the brain and leads to deterioration of cognitive functions. Patients usually experience diagnostic symptoms at later stages after irreversible neural damage occurs. Therefore, early detection of AD is crucial to start treatments to decelerate the progress of the disease and to maximize patients' quality of life. With the rapid advances in machine learning and scanning, early detection of AD may be possible via computer-assisted systems using neuroimaging data. Among all, deep learning utilizing magnetic resonance imaging (MRI) has become a prominent tool due to its capability to extract high-level features through local connectivity, weight sharing, and spatial invariance. This paper describes our investigation of the classification accuracy based on two publicly available data sets, namely, ADNI and OASIS, by building a 3D VGG variant convolutional network (CNN). We used 3D models to avoid information loss, which occurs during the process of slicing 3D MRI into 2D images and analyzing them by 2D convolutional filters. We also conducted a pre-processing of the data to enhance the effectiveness and classification performance of the model. The proposed model achieved 73.4% classification accuracy on ADNI and 69.9% on OASIS dataset with 5-fold cross-validation (CV), outperforming 2D network models.

show abstract

Automatic Recognition of Mild Cognitive Impairment and Alzheimers Disease Using Ensemble based 3D Densely Connected Convolutional Networks

Cited by 49 publications

References 34 publications

OViTAD: Optimized Vision Transformer to Predict Various Stages of Alzheimer's Disease Using Resting-State fMRI and Structural MRI Data

OViTAD: Optimized Vision Transformer to Predict Various Stages of Alzheimer's Disease Using Resting-State fMRI and Structural MRI Data

3D Brain Reconstruction by Hierarchical Shape-Perception Network from a Single Incomplete Image

3D Convolutional Neural Networks for Diagnosis of Alzheimer's Disease via Structural MRI

Contact Info

Product

Resources

About