# End-To-End Alzheimer's Disease Diagnosis and Biomarker Identification

arXiv: https://arxiv.org/abs/1810.00523 · HTML: https://mlsanity.com/paper/1810.00523/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2018.

## Built on by (most cited first, 12 of 12)

- [Combining 3D Image and Tabular Data via the Dynamic Affine Feature Map Transform](https://mlsanity.com/paper/2107.05990/) (2021)
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- [HyperFusion: A Hypernetwork Approach to Multimodal Integration of Tabular and Medical Imaging Data for Predictive Modeling](https://mlsanity.com/paper/2403.13319/) (2024)
- [Is a PET all you need? A multi-modal study for Alzheimer's disease using 3D CNNs](https://mlsanity.com/paper/2207.02094/) (2022)
- [Counterfactual Image Synthesis for Discovery of Personalized Predictive Image Markers](https://mlsanity.com/paper/2208.02311/) (2022)
- [Don't PANIC: Prototypical Additive Neural Network for Interpretable Classification of Alzheimer's Disease](https://mlsanity.com/paper/2303.07125/) (2023)
- [Abuse and Fraud Detection in Streaming Services Using Heuristic-Aware Machine Learning](https://mlsanity.com/paper/2203.02124/) (2022)
- [Loc-VAE: Learning Structurally Localized Representation from 3D Brain MR Images for Content-Based Image Retrieval](https://mlsanity.com/paper/2210.00506/) (2022)
- [Predicting recovery following stroke: deep learning, multimodal data and feature selection using explainable AI](https://mlsanity.com/paper/2310.19174/) (2023)
- [Vision Transformers and Bi-LSTM for Alzheimer's Disease Diagnosis from 3D MRI](https://mlsanity.com/paper/2401.03132/) (2024)
- [Flexible Multimodal Neuroimaging Fusion for Alzheimer's Disease Progression Prediction](https://mlsanity.com/paper/2509.12234/) (2025)
- [Attention-based Efficient Classification for 3D MRI Image of Alzheimer's Disease](https://mlsanity.com/paper/2401.14130/) (2024)

## Abstract (arXiv)

As shown in computer vision, the power of deep learning lies in automatically learning relevant and powerful features for any perdition task, which is made possible through end-to-end architectures. However, deep learning approaches applied for classifying medical images do not adhere to this architecture as they rely on several pre- and post-processing steps. This shortcoming can be explained by the relatively small number of available labeled subjects, the high dimensionality of neuroimaging data, and difficulties in interpreting the results of deep learning methods. In this paper, we propose a simple 3D Convolutional Neural Networks and exploit its model parameters to tailor the end-to-end architecture for the diagnosis of Alzheimer's disease (AD). Our model can diagnose AD with an accuracy of 94.1% on the popular ADNI dataset using only MRI data, which outperforms the previous state-of-the-art. Based on the learned model, we identify the disease biomarkers, the results of which were in accordance with the literature. We further transfer the learned model to diagnose mild cognitive impairment (MCI), the prodromal stage of AD, which yield better results compared to other methods.