# Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer's Disease

arXiv: https://arxiv.org/abs/1808.02874 · HTML: https://mlsanity.com/paper/1808.02874/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2018.

## Built on by (most cited first, 9 of 9)

- [Interpretability of Machine Learning Methods Applied to Neuroimaging](https://mlsanity.com/paper/2204.07005/) (2022)
- [Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research](https://mlsanity.com/paper/2301.08525/) (2023)
- [An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease](https://mlsanity.com/paper/2308.07778/) (2023)
- [Applications of interpretable deep learning in neuroimaging: a comprehensive review](https://mlsanity.com/paper/2406.17792/) (2024)
- [Interpretable 3D Multi-Modal Residual Convolutional Neural Network for Mild Traumatic Brain Injury Diagnosis](https://mlsanity.com/paper/2309.12572/) (2023)
- [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)
- [Exploring Regions of Interest: Visualizing Histological Image Classification for Breast Cancer using Deep Learning](https://mlsanity.com/paper/2305.20058/) (2023)
- [An explainable two-dimensional single model deep learning approach for Alzheimer's disease diagnosis and brain atrophy localization](https://mlsanity.com/paper/2107.13200/) (2021)
- [Enhancing mTBI Diagnosis with Residual Triplet Convolutional Neural Network Using 3D CT](https://mlsanity.com/paper/2311.14197/) (2023)

## Abstract (arXiv)

Visualizing and interpreting convolutional neural networks (CNNs) is an important task to increase trust in automatic medical decision making systems. In this study, we train a 3D CNN to detect Alzheimer's disease based on structural MRI scans of the brain. Then, we apply four different gradient-based and occlusion-based visualization methods that explain the network's classification decisions by highlighting relevant areas in the input image. We compare the methods qualitatively and quantitatively. We find that all four methods focus on brain regions known to be involved in Alzheimer's disease, such as inferior and middle temporal gyrus. While the occlusion-based methods focus more on specific regions, the gradient-based methods pick up distributed relevance patterns. Additionally, we find that the distribution of relevance varies across patients, with some having a stronger focus on the temporal lobe, whereas for others more cortical areas are relevant. In summary, we show that applying different visualization methods is important to understand the decisions of a CNN, a step that is crucial to increase clinical impact and trust in computer-based decision support systems.