A Novel End-to-End Hybrid Network for Alzheimer's Disease Detection Using 3D CNN and 3D CLSTM

Xia, Zaimin; Yue, Guanghui; Xu, Yanwu; Feng, Chiyu; Yang, Mengya; Wang, Tianfu; Lei, Baiying

doi:10.1109/isbi45749.2020.9098621

Cited by 29 publications

(20 citation statements)

References 10 publications

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“…Deep learning research for early diagnosis of AD comprises of two frequently encountered types of studies. There is a substantial amount of work regarding CNN architectures for feature extraction and segmentation from neuroimaging (Basheera and Ram 2020;Amini et al 2021;Wang, Chen, and Subbalakshmi 2020;Parmar et al 2020;Abdu-lAzeem, Bahgat, and Badawy 2021;Xia et al 2020). These studies utilize effective deep frameworks to predict AD diagnosis.…”

Section: Deep Learning For Neurodegenerative Diseasesmentioning

confidence: 99%

Investigating Conversion from Mild Cognitive Impairment to Alzheimer's Disease using Latent Space Manipulation

Ayvaz¹,

Baytaş²

2021

Preprint

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Alzheimer's disease is the most common cause of dementia that affects millions of lives worldwide. Investigating the underlying causes and risk factors of Alzheimer's disease is essential to prevent its progression. Mild Cognitive Impairment (MCI) is considered an intermediate stage before Alzheimer's disease. Early prediction of the conversion from the MCI to Alzheimer's is crucial to take necessary precautions for decelerating the progression and developing suitable treatments. In this study, we propose a deep learning framework to discover the variables which are identifiers of the conversion from MCI to Alzheimer's disease. In particular, the latent space of a variational auto-encoder network trained with the MCI and Alzheimer's patients is manipulated to obtain the significant attributes and decipher their behavior that leads to the conversion from MCI to Alzheimer's disease. By utilizing a generative decoder and the dimensions that lead to the Alzheimer's diagnosis, we generate synthetic dementia patients from MCI patients in the dataset. Experimental results show promising quantitative and qualitative results on one of the most extensive and commonly used Alzheimer's disease neuroimaging datasets in literature.

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Section: Deep Learning For Neurodegenerative Diseasesmentioning

confidence: 99%

Investigating Conversion from Mild Cognitive Impairment to Alzheimer's Disease using Latent Space Manipulation

Ayvaz¹,

Baytaş²

2021

Preprint

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“…Currently, convolutional neural networks are widely used in the field of medical diagnosis ( Fang et al, 2020 ; Xia et al, 2020 ; Yang et al, 2021 ). This study attempts to use a new type of convolutional neural network (OT-Unet) to automatically segment PPA.…”

Section: Introductionmentioning

confidence: 99%

Optimized-Unet: Novel Algorithm for Parapapillary Atrophy Segmentation

Wan

Han

et al. 2021

Front. Neurosci.

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In recent years, an increasing number of people have myopia in China, especially the younger generation. Common myopia may develop into high myopia. High myopia causes visual impairment and blindness. Parapapillary atrophy (PPA) is a typical retinal pathology related to high myopia, which is also a basic clue for diagnosing high myopia. Therefore, accurate segmentation of the PPA is essential for high myopia diagnosis and treatment. In this study, we propose an optimized Unet (OT-Unet) to solve this important task. OT-Unet uses one of the pre-trained models: Visual Geometry Group (VGG), ResNet, and Res2Net, as a backbone and is combined with edge attention, parallel partial decoder, and reverse attention modules to improve the segmentation accuracy. In general, using the pre-trained models can improve the accuracy with fewer samples. The edge attention module extracts contour information, the parallel partial decoder module combines the multi-scale features, and the reverse attention module integrates high- and low-level features. We also propose an augmented loss function to increase the weight of complex pixels to enable the network to segment more complex lesion areas. Based on a dataset containing 360 images (Including 26 pictures provided by PALM), the proposed OT-Unet achieves a high AUC (Area Under Curve) of 0.9235, indicating a significant improvement over the original Unet (0.7917).

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“…Over 50 million people worldwide suffer from dementia and Alzheimer's Disease (AD) is the most prevailing form [1], [2], killing more people than prostate and breast cancers combined [3]. AD is a neurological brain disorder that begins with Mild Cognitive Impairment (MCI) and worsens progressively until death, as it ends up destroying the brain area that controls breathing and heart functions [4], [5].…”

Section: Introductionmentioning

confidence: 99%

An End-to-End 3D ConvLSTM-based Framework for Early Diagnosis of Alzheimer's Disease from Full-Resolution Whole-Brain sMRI Scans

Tomassini

Falcionelli

Sernani

et al. 2021

2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS)

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Alzheimer's Disease (AD) is the most prevailing form of dementia, killing more people than prostate and breast cancers combined. Structural Magnetic Resonance Imaging (sMRI) is widely used for the analysis of progressive brain aggravation and its clinical utility in discriminating AD is well established. Even if an effective cure does not exist yet, early detection is fundamental for slowing down the worsening of symptoms. Thus, the aim of the present work is to propose an end-to-end 3D Convolutional Long Short-Term Memory (ConvLSTM)-based framework for early diagnosis of AD from full-resolution whole-brain sMRI scans. The proposed framework was applied to 427 full-resolution whole-brain sMRI scans belonging to both OASIS and ADNI databases in order to provide a less dataset-specific approach. Results show that our framework is performing well in discriminating AD from Cognitively Normal (CN) patients, reaching a classification accuracy of 86%, sensitivity of 96%, f1-score of 88% and AUC of 93% on the test data. The tests were performed on a scalable GPU cloud service and are publicly available to guarantee reproducibility. Since the proposed framework performs well without domain-specific knowledge from AD as well as computationally-costly processes such as segmentation, it can be applied to other mental disorders using whole-brain sMRI scans as input data.

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A Novel End-to-End Hybrid Network for Alzheimer's Disease Detection Using 3D CNN and 3D CLSTM

Cited by 29 publications

References 10 publications

Investigating Conversion from Mild Cognitive Impairment to Alzheimer's Disease using Latent Space Manipulation

Investigating Conversion from Mild Cognitive Impairment to Alzheimer's Disease using Latent Space Manipulation

Optimized-Unet: Novel Algorithm for Parapapillary Atrophy Segmentation

An End-to-End 3D ConvLSTM-based Framework for Early Diagnosis of Alzheimer's Disease from Full-Resolution Whole-Brain sMRI Scans

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