Alzheimer's Disease Brain MRI Classification: Challenges and Insights
This work addresses reproducibility issues in medical imaging research for Alzheimer's Disease, highlighting incremental insights into data handling challenges.
The study tackled the problem of inconsistent performance in Alzheimer's Disease MRI classification by revealing that subject-level data splitting leads to lower results than previously reported, questioning the validity of prior studies; they tested three splitting methods and reported results using all available subjects from the ADNI dataset.
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.