jrieke/cnn-interpretability
🏥 Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer’s Disease
This project helps medical researchers and clinicians understand how their deep learning models diagnose Alzheimer's disease from structural MRI scans. You input a trained 3D convolutional neural network (CNN) and an MRI scan, and it outputs visual heatmaps highlighting the specific brain regions that most influenced the model's diagnostic decision. This is ideal for neuroimaging specialists, radiologists, or anyone involved in medical AI development and validation.
178 stars. No commits in the last 6 months.
Use this if you need to visualize and interpret the decisions of a 3D convolutional neural network for diagnosing Alzheimer's disease from MRI scans, enhancing trust in automated medical systems.
Not ideal if your primary goal is to train a new diagnostic model from scratch or if you are not working with PyTorch models and MRI data.
Stars
178
Forks
51
Language
Jupyter Notebook
License
BSD-2-Clause
Category
Last pushed
Jul 05, 2019
Commits (30d)
0
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