Alzheimer's disease diagnostics by adaptation of 3D convolutional network

By Ehsan Hosseini-Asl, Robert S. Keynton, Ayman S. El-Baz. International Conference on Information Photonics, 2016

314 citations recorded by the source corpus.

Early diagnosis, playing an important role in preventing progress and treating the Alzheimer's disease (AD), is based on classification of features extracted from brain images. The features have to accurately capture main AD-related variations of anatomical brain structures, such as, e.g., ventricles size, hippocampus shape, cortical thickness, and brain volume. This paper proposed to predict the AD with a deep 3D convolutional neural network (3D-CNN), which can learn generic features capturing AD biomarkers and adapt to different domain datasets. The 3D-CNN is built upon a 3D convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans. Fully connected upper layers of the 3D-CNN are then fine-tuned for each task-specific AD classification. Experiments on the CADDementia MRI dataset with no skull-stripping preprocessing have shown our 3D-CNN outperforms several conventional classifiers by accuracy. Abilities of the 3D-CNN to generalize the features learnt and adapt to other domains have been validated on the ADNI dataset.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/d67ace21b124af25d9d0426be9f7bf0d1ff9736a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Yanqing Zhang
2. Yaping Wang
3. Manhua Liu
4. Danni Cheng
5. Jyoti Islam
6. Kundong Wang

In Computer Vision and Pattern Recognition

1. Jorge Samper-González
2. Elina Thibeau-Sutre
3. Yanqing Zhang
4. Junhao Wen
5. Yaping Wang
6. Alexandre Routier
7. Olivier Colliot
8. Stanley Durrleman

In Neurology

1. Yanqing Zhang
2. Manhua Liu
3. Jyoti Islam
4. Kundong Wang
5. Arvid Lundervold

Citations in the corpus

• Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks (2018, methodology): “Hosseini-Asl et al. [74] adapted a 3D CNN model for AD diagnostics.”
• FastSurfer - A fast and accurate deep learning based neuroimaging pipeline (2019, unknown): “…and Savarimuthu, 2017; Havaei et al., 2017; Amin et al., 2018; Brainless Glioma , 2018), brain lesion segmentation (Kamnitsas et al., 2017; Varghese et al., 2017; Rezaei et al., 2017; Roa-Barco et al., 2017; Chen and Konukoglu, 2018), MR image reconstruction (Jin et al., 2017; Mardani et…”
• A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer’s disease (2018, methodology): “While previous methods employ pre-training (Payan and Montana, 2015; Hosseini-Asl et al., 2016; Liu et al., 2018) to reap similar bene fi ts, this requires training the model twice, whereas dual-learning is a single-stage procedure which facilitate training.”
• Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer’s Disease Diagnosis (2018, methodology): “For fair comparison, we downloaded the source codes released by the authors of (Hosseini-Asl et al. 2016) in the website and implemented it with our best effort by using each single modality and multi-modality of our data set.”
• Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation (2019, methodology): “However, except for (Manhua Liu et al., 2018) where the code provided by (Hosseini-Asl et al., 2016) is reused, the methods used for the comparison are poorly described and the studies would thus be difficult, if not impossible, to reproduce.”
• Domain Adaptation for Medical Image Analysis: A Survey (2021, unknown): “recognition [15, 2, 7, 10], and feature learning [14, 12, 22]”