# An Explainable 3D Residual Self-Attention Deep Neural Network FOR Joint Atrophy Localization and Alzheimer's Disease Diagnosis using Structural MRI

arXiv: https://arxiv.org/abs/2008.04024 · HTML: https://mlsanity.com/paper/2008.04024/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2020.

## Built on by (most cited first, 27 of 27)

- [Learn-Explain-Reinforce: Counterfactual Reasoning and Its Guidance to Reinforce an Alzheimer's Disease Diagnosis Model](https://mlsanity.com/paper/2108.09451/) (2021)
- [Is attention all you need in medical image analysis? A review](https://mlsanity.com/paper/2307.12775/) (2023)
- [ADAgent: LLM Agent for Alzheimer's Disease Analysis with Collaborative Coordinator](https://mlsanity.com/paper/2506.11150/) (2025)
- [SiSPRNet: End-to-End Learning for Single-Shot Phase Retrieval](https://mlsanity.com/paper/2205.11434/) (2022)
- [Eat-Radar: Continuous Fine-Grained Intake Gesture Detection Using FMCW Radar and 3D Temporal Convolutional Network with Attention](https://mlsanity.com/paper/2211.04253/) (2022)
- [sMRI-PatchNet: A novel explainable patch-based deep learning network for Alzheimer's disease diagnosis and discriminative atrophy localisation with Structural MRI](https://mlsanity.com/paper/2302.08967/) (2023)
- [Unmasking Dementia Detection by Masking Input Gradients: A JSM Approach to Model Interpretability and Precision](https://mlsanity.com/paper/2402.16008/) (2024)
- [Enhancing MRI-Based Classification of Alzheimer's Disease with Explainable 3D Hybrid Compact Convolutional Transformers](https://mlsanity.com/paper/2403.16175/) (2024)
- [Applications of interpretable deep learning in neuroimaging: a comprehensive review](https://mlsanity.com/paper/2406.17792/) (2024)
- [Layer-Wise Partitioning and Merging for Efficient and Scalable Deep Learning](https://mlsanity.com/paper/2207.11019/) (2022)
- [Smart ROI Detection for Alzheimer's disease prediction using explainable AI](https://mlsanity.com/paper/2303.10401/) (2023)
- [Explainable AI is Responsible AI: How Explainability Creates Trustworthy and Socially Responsible Artificial Intelligence](https://mlsanity.com/paper/2312.01555/) (2023)
- [Regional Deep Atrophy: a Self-Supervised Learning Method to Automatically Identify Regions Associated With Alzheimer's Disease Progression From Longitudinal MRI](https://mlsanity.com/paper/2304.04673/) (2023)
- [A Quantitatively Interpretable Model for Alzheimer's Disease Prediction Using Deep Counterfactuals](https://mlsanity.com/paper/2310.03457/) (2023)
- [INSIGHT: Explainable Weakly-Supervised Medical Image Analysis](https://mlsanity.com/paper/2412.02012/) (2024)
- [EffNetViTLoRA: An Efficient Hybrid Deep Learning Approach for Alzheimer's Disease Diagnosis](https://mlsanity.com/paper/2508.19349/) (2025)
- [LAP: An Attention-Based Module for Concept Based Self-Interpretation and Knowledge Injection in Convolutional Neural Networks](https://mlsanity.com/paper/2201.11808/) (2022)
- [Automatic Classification of Alzheimer's Disease using brain MRI data and deep Convolutional Neural Networks](https://mlsanity.com/paper/2204.00068/) (2022)
- [Deep Grading based on Collective Artificial Intelligence for AD Diagnosis and Prognosis](https://mlsanity.com/paper/2211.15192/) (2022)
- [A reproducible 3D convolutional neural network with dual attention module (3D-DAM) for Alzheimer's disease classification](https://mlsanity.com/paper/2310.12574/) (2023)
- [Multimodal Identification of Alzheimer's Disease: A Review](https://mlsanity.com/paper/2311.12842/) (2023)
- [Unifying Interpretability and Explainability for Alzheimer's Disease Progression Prediction](https://mlsanity.com/paper/2406.07777/) (2024)
- [The Role of Explainable AI in Revolutionizing Human Health Monitoring: A Review](https://mlsanity.com/paper/2409.07347/) (2024)
- [AlzhiNet: Traversing from 2DCNN to 3DCNN, Towards Early Detection and Diagnosis of Alzheimer's Disease](https://mlsanity.com/paper/2410.02714/) (2024)
- [XDementNET: An Explainable Attention Based Deep Convolutional Network to Detect Alzheimer Progression from MRI data](https://mlsanity.com/paper/2505.13906/) (2025)
- [An Interpretable Multi-Plane Fusion Framework With Kolmogorov-Arnold Network Guided Attention Enhancement for Alzheimer's Disease Diagnosis](https://mlsanity.com/paper/2508.06157/) (2025)
- [Anatomical Grounding and Leakage-Aware Multimodal Contrastive Learning for Alzheimer's Disease Classification from Structural MRI](https://mlsanity.com/paper/2609.15888/) (2026)

## Abstract (arXiv)

Computer-aided early diagnosis of Alzheimer's disease (AD) and its prodromal form mild cognitive impairment (MCI) based on structure Magnetic Resonance Imaging (sMRI) has provided a cost-effective and objective way for early prevention and treatment of disease progression, leading to improved patient care. In this work, we have proposed a novel computer-aided approach for early diagnosis of AD by introducing an explainable 3D Residual Attention Deep Neural Network (3D ResAttNet) for end-to-end learning from sMRI scans. Different from the existing approaches, the novelty of our approach is three-fold: 1) A Residual Self-Attention Deep Neural Network has been proposed to capture local, global and spatial information of MR images to improve diagnostic performance; 2) An explanation method using Gradient-based Localization Class Activation mapping (Grad-CAM) has been introduced to improve the explainable of the proposed method; 3) This work has provided a full end-to-end learning solution for automated disease diagnosis. Our proposed 3D ResAttNet method has been evaluated on a large cohort of subjects from real datasets for two changeling classification tasks (i.e., Alzheimer's disease (AD) vs. Normal cohort (NC) and progressive MCI (pMCI) vs. stable MCI (sMCI)). The experimental results show that the proposed approach has a competitive advantage over the state-of-the-art models in terms of accuracy performance and generalizability. The explainable mechanism in our approach is able to identify and highlight the contribution of the important brain parts (e.g., hippocampus, lateral ventricle and most parts of the cortex) for transparent decisions.