# Interpretable differential diagnosis for Alzheimer's disease and Frontotemporal dementia

arXiv: https://arxiv.org/abs/2206.07417 · HTML: https://mlsanity.com/paper/2206.07417/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 39 references that are in the map.
- First posted on arXiv 15 Jun 2022.

## Builds on (most cited first, 2 of 2)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [AssemblyNet: A large ensemble of CNNs for 3D Whole Brain MRI Segmentation](https://mlsanity.com/paper/1911.09098/) (2019)

## Built on by (most cited first, 8 of 8)

- [Don't PANIC: Prototypical Additive Neural Network for Interpretable Classification of Alzheimer's Disease](https://mlsanity.com/paper/2303.07125/) (2023)
- [DiaMond: Dementia Diagnosis with Multi-Modal Vision Transformers Using MRI and PET](https://mlsanity.com/paper/2410.23219/) (2024)
- [Deep grading for MRI-based differential diagnosis of Alzheimer's disease and Frontotemporal dementia](https://mlsanity.com/paper/2211.14096/) (2022)
- [Enhancing MRI-Based Classification of Alzheimer's Disease with Explainable 3D Hybrid Compact Convolutional Transformers](https://mlsanity.com/paper/2403.16175/) (2024)
- [From Barlow Twins to Triplet Training: Differentiating Dementia with Limited Data](https://mlsanity.com/paper/2404.06253/) (2024)
- [Applications of interpretable deep learning in neuroimaging: a comprehensive review](https://mlsanity.com/paper/2406.17792/) (2024)
- [3D Transformer based on deformable patch location for differential diagnosis between Alzheimer's disease and Frontotemporal dementia](https://mlsanity.com/paper/2309.03183/) (2023)
- [Pro-t-VAE: Closed-Form Student's t Mixtures for Robust Prototype Discovery](https://mlsanity.com/paper/2610.14894/) (2026)

## Abstract (arXiv)

Alzheimer's disease and Frontotemporal dementia are two major types of dementia. Their accurate diagnosis and differentiation is crucial for determining specific intervention and treatment. However, differential diagnosis of these two types of dementia remains difficult at the early stage of disease due to similar patterns of clinical symptoms. Therefore, the automatic classification of multiple types of dementia has an important clinical value. So far, this challenge has not been actively explored. Recent development of deep learning in the field of medical image has demonstrated high performance for various classification tasks. In this paper, we propose to take advantage of two types of biomarkers: structure grading and structure atrophy. To this end, we propose first to train a large ensemble of 3D U-Nets to locally discriminate healthy versus dementia anatomical patterns. The result of these models is an interpretable 3D grading map capable of indicating abnormal brain regions. This map can also be exploited in various classification tasks using graph convolutional neural network. Finally, we propose to combine deep grading and atrophy-based classifications to improve dementia type discrimination. The proposed framework showed competitive performance compared to state-of-the-art methods for different tasks of disease detection and differential diagnosis.