# Alzheimer's Disease Brain MRI Classification: Challenges and Insights

arXiv: https://arxiv.org/abs/1906.04231 · HTML: https://mlsanity.com/paper/1906.04231/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2019.

## Built on by (most cited first, 3 of 3)

- [Brain-Aware Replacements for Supervised Contrastive Learning in Detection of Alzheimer's Disease](https://mlsanity.com/paper/2207.04574/) (2022)
- [Fusion of medical imaging and electronic health records with attention and multi-head machanisms](https://mlsanity.com/paper/2112.11710/) (2021)
- [Deep Joint Learning of Pathological Region Localization and Alzheimer's Disease Diagnosis](https://mlsanity.com/paper/2108.04555/) (2021)

## Abstract (arXiv)

In recent years, many papers have reported state-of-the-art performance on Alzheimer's Disease classification with MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset using convolutional neural networks. However, we discover that when we split that data into training and testing sets at the subject level, we are not able to obtain similar performance, bringing the validity of many of the previous studies into question. Furthermore, we point out that previous works use different subsets of the ADNI data, making comparison across similar works tricky. In this study, we present the results of three splitting methods, discuss the motivations behind their validity, and report our results using all of the available subjects.