# Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification

arXiv: https://arxiv.org/abs/1803.02544 · HTML: https://mlsanity.com/paper/1803.02544/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2018.

## Built on by (most cited first, 14 of 14)

- [Cascaded Multi-Modal Mixing Transformers for Alzheimer's Disease Classification with Incomplete Data](https://mlsanity.com/paper/2210.00255/) (2022)
- [IMFNet: Interpretable Multimodal Fusion for Point Cloud Registration](https://mlsanity.com/paper/2111.09624/) (2021)
- [Longformer: Longitudinal Transformer for Alzheimer's Disease Classification with Structural MRIs](https://mlsanity.com/paper/2302.00901/) (2023)
- [ADAPT: Alzheimer Diagnosis through Adaptive Profiling Transformers](https://mlsanity.com/paper/2401.06349/) (2024)
- [Applications of interpretable deep learning in neuroimaging: a comprehensive review](https://mlsanity.com/paper/2406.17792/) (2024)
- [One Wave To Explain Them All: A Unifying Perspective On Feature Attribution](https://mlsanity.com/paper/2410.01482/) (2024)
- [Interpretable Multimodal Learning for Cardiovascular Hemodynamics Assessment](https://mlsanity.com/paper/2404.04718/) (2024)
- [Machine Learning Workflow to Explain Black-box Models for Early Alzheimer's Disease Classification Evaluated for Multiple Datasets](https://mlsanity.com/paper/2205.05907/) (2022)
- [XADLiME: eXplainable Alzheimer's Disease Likelihood Map Estimation via Clinically-guided Prototype Learning](https://mlsanity.com/paper/2207.13223/) (2022)
- [Exploring Regions of Interest: Visualizing Histological Image Classification for Breast Cancer using Deep Learning](https://mlsanity.com/paper/2305.20058/) (2023)
- [SE3D: A Framework For Saliency Method Evaluation In 3D Imaging](https://mlsanity.com/paper/2405.14584/) (2024)
- [Machine vision detection to daily facial fatigue with a nonlocal 3D attention network](https://mlsanity.com/paper/2104.10420/) (2021)
- [AlzhiNet: Traversing from 2DCNN to 3DCNN, Towards Early Detection and Diagnosis of Alzheimer's Disease](https://mlsanity.com/paper/2410.02714/) (2024)
- [A Hybrid CNN and ML Framework for Multi-modal Classification of Movement Disorders Using MRI and Brain Structural Features](https://mlsanity.com/paper/2602.05574/) (2026)

## Abstract (arXiv)

We develop three efficient approaches for generating visual explanations from 3D convolutional neural networks (3D-CNNs) for Alzheimer's disease classification. One approach conducts sensitivity analysis on hierarchical 3D image segmentation, and the other two visualize network activations on a spatial map. Visual checks and a quantitative localization benchmark indicate that all approaches identify important brain parts for Alzheimer's disease diagnosis. Comparative analysis show that the sensitivity analysis based approach has difficulty handling loosely distributed cerebral cortex, and approaches based on visualization of activations are constrained by the resolution of the convolutional layer. The complementarity of these methods improves the understanding of 3D-CNNs in Alzheimer's disease classification from different perspectives.