Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks

By Adrien Payan, Giovanni Montana. International Conference on Pattern Recognition Applications and Methods, 2015

530 citations recorded by the source corpus.

Pattern recognition methods using neuroimaging data for the diagnosis of Alzheimer's disease have been the subject of extensive research in recent years. In this paper, we use deep learning methods, and in particular sparse autoencoders and 3D convolutional neural networks, to build an algorithm that can predict the disease status of a patient, based on an MRI scan of the brain. We report on experiments using the ADNI data set involving 2,265 historical scans. We demonstrate that 3D convolutional neural networks outperform several other classifiers reported in the literature and produce state-of-art results.

Subfields: Biomedical Engineering, Psychiatry and Mental health, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/07170b3bc117c84246adda15a4a1626785f826ca

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In Biomedical Engineering

In Psychiatry and Mental health

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Attentional ShapeContextNet for Point Cloud Recognition (2018, background): “Extending 2D-based convolution to 3D-based convolution for 3D computer vision applications such as 3D medical imaging [23, 10], though still effective, is arguably less explosive than the 2D cases.”
Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker (2016, background): “Recently, 3D convolutional neural networks have been also proposed for Alzheimer's disease classification (Payan and Montana, 2015; Sarraf and Tofighi, 2016), brain lesion segmentation (Kamnitsas et al., 2017) and skull stripping (Kleesiek et al., 2016).”
Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks (2018, methodology): “Payan and Montana [65] trained sparse autoencoders and 3D CNN model for AD diagnosis.”
Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications (2017, unknown): “…(Gao and Hui, 2016), as well as with small (Gao and Hui, 2016; Sarraf and Toﬁghi, 2016) and large (Gupta et al., 2013; Hosseini-Asl et al., 2016; Payan and Montana, 2015) sample sizes Hosseini-Asl et al. (2016) used an alternative and interesting approach which involved pre-training a CNN in…”
Medical Image Analysis using Convolutional Neural Networks: A Review (2017, methodology): “Payan [99] Prediction of Alzheimer Disease CNN with 2D Convolutions and 3D Convolutions MRI Images 89.”
Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer’s Disease Diagnosis (2018, methodology): “Adrien et al., (Adrien 2015) proposed a deep learning based classification algorithm for AD diagnosis using both structural and functional MRI.”
Deep Learning Approach for Early Detection of Alzheimer’s Disease (2021, background): “[22] developed the work presented in [19].”
Deep Learning and Medical Diagnosis: A Review of Literature (2018, unknown): “[21] CNN MRI Alzheimer’s disease prediction; the accuracy of this approach is far superior compared to 2D methods”
A review of the application of deep learning in medical image classification and segmentation (2020, background): “(34) proposed a 3D CNN for AD diagnosis based on SAE pre-training.”
Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease (2018, background): “a new feature space with learning algorithms, among which deep learning (DL) has achieved the state-of-the-art performance [20], [24].”
FastSurfer - A fast and accurate deep learning based neuroimaging pipeline (2019, unknown): “…Mardani et al., 2019; Schlemper et al., 2018; Yang et al., 2018; Dedmari et al., 2018), prediction of brain related diseases and their progression (Payan and Montana, 2015; Qi and Tejedor, 2016; Hossei-ni-Asl et al., 2016; Lee et al. Kim) or segmentation of a smaller number of brain…”
DeepAD: Alzheimer’s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI (2016, methodology): “of Imperial College London designed (Payan and Montana, 2015) a predictive algorithm to distinguish AD MCI from normal healthy control subjects'imaging.”
Structured Receptive Fields in CNNs (2016, background): “Note that [7, 24] train on their own subset and use an order of magnitude more training data.”