# Deep grading for MRI-based differential diagnosis of Alzheimer's disease and Frontotemporal dementia

arXiv: https://arxiv.org/abs/2211.14096 · HTML: https://mlsanity.com/paper/2211.14096/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 43 references that are in the map.
- First posted on arXiv 25 Nov 2022.

## Builds on (most cited first, 5 of 5)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [AssemblyNet: A large ensemble of CNNs for 3D Whole Brain MRI Segmentation](https://mlsanity.com/paper/1911.09098/) (2019)
- [Interpretable differential diagnosis for Alzheimer's disease and Frontotemporal dementia](https://mlsanity.com/paper/2206.07417/) (2022)
- [Graph of brain structures grading for early detection of Alzheimer's disease](https://mlsanity.com/paper/1807.03173/) (2018)

## Built on by (most cited first, 5 of 5)

- [Brain Structure Ages -- A new biomarker for multi-disease classification](https://mlsanity.com/paper/2304.06591/) (2023)
- [Lifespan tree of brain anatomy: diagnostic values for motor and cognitive neurodegenerative diseases](https://mlsanity.com/paper/2502.09682/) (2025)
- [petBrain: A New Pipeline for Amyloid, Tau Tangles and Neurodegeneration Quantification Using PET and MRI](https://mlsanity.com/paper/2506.03217/) (2025)
- [Beyond Black-Box AI: Interpretable Hybrid Systems for Dementia Care](https://mlsanity.com/paper/2507.01282/) (2025)
- [CAHAL: Clinically Applicable resolution enHAncement for Low-resolution MRI scans](https://mlsanity.com/paper/2604.18781/) (2026)

## Abstract (arXiv)

Alzheimer's disease and Frontotemporal dementia are common forms of neurodegenerative dementia. Behavioral alterations and cognitive impairments are found in the clinical courses of both diseases and their differential diagnosis is sometimes difficult for physicians. Therefore, an accurate tool dedicated to this diagnostic challenge can be valuable in clinical practice. However, current structural imaging methods mainly focus on the detection of each disease but rarely on their differential diagnosis. In this paper, we propose a deep learning based approach for both problems of disease detection and differential diagnosis. We suggest utilizing two types of biomarkers for this application: structure grading and structure atrophy. First, we propose to train a large ensemble of 3D U-Nets to locally determine the anatomical patterns of healthy people, patients with Alzheimer's disease and patients with Frontotemporal dementia using structural MRI as input. The output of the ensemble is a 2-channel disease's coordinate map able to be transformed into a 3D grading map which is easy to interpret for clinicians. This 2-channel map is coupled with a multi-layer perceptron classifier for different classification tasks. Second, we propose to combine our deep learning framework with a traditional machine learning strategy based on volume to improve the model discriminative capacity and robustness. After both cross-validation and external validation, our experiments based on 3319 MRI demonstrated competitive results of our method compared to the state-of-the-art methods for both disease detection and differential diagnosis.