# Enhancing MRI-Based Classification of Alzheimer's Disease with Explainable 3D Hybrid Compact Convolutional Transformers

arXiv: https://arxiv.org/abs/2403.16175 · HTML: https://mlsanity.com/paper/2403.16175/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 50 references that are in the map.
- First posted on arXiv 24 Mar 2024.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Escaping the Big Data Paradigm with Compact Transformers](https://mlsanity.com/paper/2104.05704/) (2021)
- [Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification](https://mlsanity.com/paper/1701.06643/) (2017)
- [An Explainable 3D Residual Self-Attention Deep Neural Network FOR Joint Atrophy Localization and Alzheimer's Disease Diagnosis using Structural MRI](https://mlsanity.com/paper/2008.04024/) (2020)
- [Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks](https://mlsanity.com/paper/1502.02506/) (2015)
- [Alzheimer's Disease Diagnostics by Adaptation of 3D Convolutional Network](https://mlsanity.com/paper/1607.00455/) (2016)
- [Alzheimer's Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network](https://mlsanity.com/paper/1607.00556/) (2016)
- [Interpretable differential diagnosis for Alzheimer's disease and Frontotemporal dementia](https://mlsanity.com/paper/2206.07417/) (2022)
- [Adversarially-regularized mixed effects deep learning (ARMED) models for improved interpretability, performance, and generalization on clustered data](https://mlsanity.com/paper/2202.11783/) (2022)
- [Brain-Aware Replacements for Supervised Contrastive Learning in Detection of Alzheimer's Disease](https://mlsanity.com/paper/2207.04574/) (2022)
- [Neural Pre-Processing: A Learning Framework for End-to-end Brain MRI Pre-processing](https://mlsanity.com/paper/2303.12148/) (2023)
- [Fundamental Limits of Deep Graph Convolutional Networks](https://mlsanity.com/paper/1910.12954/) (2019)
- [Transferring Models Trained on Natural Images to 3D MRI via Position Encoded Slice Models](https://mlsanity.com/paper/2303.01491/) (2023)

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

- [Triad: Vision Foundation Model for 3D Magnetic Resonance Imaging](https://mlsanity.com/paper/2502.14064/) (2025)
- [Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications](https://mlsanity.com/paper/2508.07165/) (2025)
- [NeuroAPS-Net: Neuro-Anatomically Aware Point Cloud Representation for Efficient Alzheimer's Disease Classification](https://mlsanity.com/paper/2604.22883/) (2026)

## Abstract (arXiv)

Alzheimer's disease (AD), characterized by progressive cognitive decline and memory loss, presents a formidable global health challenge, underscoring the critical importance of early and precise diagnosis for timely interventions and enhanced patient outcomes. While MRI scans provide valuable insights into brain structures, traditional analysis methods often struggle to discern intricate 3D patterns crucial for AD identification. Addressing this challenge, we introduce an alternative end-to-end deep learning model, the 3D Hybrid Compact Convolutional Transformers 3D (HCCT). By synergistically combining convolutional neural networks (CNNs) and vision transformers (ViTs), the 3D HCCT adeptly captures both local features and long-range relationships within 3D MRI scans. Extensive evaluations on prominent AD benchmark dataset, ADNI, demonstrate the 3D HCCT's superior performance, surpassing state of the art CNN and transformer-based methods in classification accuracy. Its robust generalization capability and interpretability marks a significant stride in AD classification from 3D MRI scans, promising more accurate and reliable diagnoses for improved patient care and superior clinical outcomes.