ArticleMachine learning in medical imaging. MLMI (Workshop)2019
Confounder-Aware Visualization of ConvNets.
Article in Machine learning in medical imaging. MLMI (Workshop), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- Leveraging Machine Learning to Advance Alcohol Research: Current Applications, Challenges, and Opportunities.Alcohol research : current reviews · 2026Review
- Applications of interpretable deep learning in neuroimaging: A comprehensive review.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Multiple Instance Neuroimage Transformer.Predictive Intelligence in Medicine. PRIME (Workshop) · 2022Article
- Representation Disentanglement for Multi-modal Brain MRI Analysis.Information processing in medical imaging : proceedings of the ... conference · 2021Article
- Longitudinal Pooling & Consistency Regularization to Model Disease Progression From MRIs.IEEE journal of biomedical and health informatics · 2021Article
- Metadata Normalization.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2021Article
- Training confounder-free deep learning models for medical applications.Nature communications · 2020Article
- Spatio-Temporal Graph Convolution for Resting-State fMRI Analysis.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
With recent advances in deep learning, neuroimaging studies increasingly rely on convolutional networks (ConvNets) to predict diagnosis based on MR images. To gain a better understanding of how a disease impacts the brain, the studies visualize the salience maps of the ConvNet highlighting voxels within the brain majorly contributing to the prediction. However, these salience maps are generally confounded, i.e., some salient regions are more predictive of confounding variables (such as age) than the diagnosis. To avoid such misinterpretation, we propose in this paper an approach that aims to visualize confounder-free saliency maps that only highlight voxels predictive of the diagnosis. The approach incorporates univariate statistical tests to identify confounding effects within the intermediate features learned by ConvNet. The influence from the subset of confounded features is then removed by a novel partial back-propagation procedure. We use this two-step approach to visualize confounder-free saliency maps extracted from synthetic and two real datasets. These experiments reveal the potential of our visualization in producing unbiased model-interpretation.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.