# Alzheimer's Disease Diagnostics by Adaptation of 3D Convolutional Network

arXiv: https://arxiv.org/abs/1607.00455 · HTML: https://mlsanity.com/paper/1607.00455/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2016.

## Built on by (most cited first, 13 of 13)

- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration](https://mlsanity.com/paper/2209.05160/) (2022)
- [Automatic Assessment of Alzheimer's Disease Diagnosis Based on Deep Learning Techniques](https://mlsanity.com/paper/2105.08446/) (2021)
- [Navigating Distribution Shifts in Medical Image Analysis: A Survey](https://mlsanity.com/paper/2411.05824/) (2024)
- [Enhancing MRI-Based Classification of Alzheimer's Disease with Explainable 3D Hybrid Compact Convolutional Transformers](https://mlsanity.com/paper/2403.16175/) (2024)
- [Vision Transformers and Bi-LSTM for Alzheimer's Disease Diagnosis from 3D MRI](https://mlsanity.com/paper/2401.03132/) (2024)
- [Dementia Severity Classification under Small Sample Size and Weak Supervision in Thick Slice MRI](https://mlsanity.com/paper/2103.10056/) (2021)
- [AI in Telemedicine: An Appraisal on Deep Learning-Based Approaches to Virtual Diagnostic Solutions (VDS)](https://mlsanity.com/paper/2208.04690/) (2022)
- [Deep Grading based on Collective Artificial Intelligence for AD Diagnosis and Prognosis](https://mlsanity.com/paper/2211.15192/) (2022)
- [Towards Practical Application of Deep Learning in Diagnosis of Alzheimer's Disease](https://mlsanity.com/paper/2212.04528/) (2022)
- [TIDo: Source-free Task Incremental Learning in Non-stationary Environments](https://mlsanity.com/paper/2301.12055/) (2023)
- [Early Detection of Alzheimer's Disease using Bottleneck Transformers](https://mlsanity.com/paper/2305.00923/) (2023)
- [AlzhiNet: Traversing from 2DCNN to 3DCNN, Towards Early Detection and Diagnosis of Alzheimer's Disease](https://mlsanity.com/paper/2410.02714/) (2024)

## Abstract (arXiv)

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.