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Implementation of model explainability for a basic brain tumor detection using convolutional neural networks on MRI slices



While neural networks gain popularity in medical research, attempts to make the decisions of a model explainable are often only made towards the end of the development process once a high predictive accuracy has been achieved.


In order to assess the advantages of implementing features to increase explainability early in the development process, we trained a neural network to differentiate between MRI slices containing either a vestibular schwannoma, a glioblastoma, or no tumor.


Making the decisions of a network more explainable helped to identify potential bias and choose appropriate training data.


Model explainability should be considered in early stages of training a neural network for medical purposes as it may save time in the long run and will ultimately help physicians integrate the network’s predictions into a clinical decision.

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Code availability

The trained model can be provided by the corresponding author.

The Bayesian neural network was implemented based on

The Grad-CAM implementation can be found at

The pretrained resnet50 was imported from the fastai library.

The IXI dataset can be downloaded from

The glioblastoma dataset can be downloaded from


No funding was received for this project.

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Correspondence to Paul Windisch.

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Conflict of Interest

Christoph Fürweger received speaker honoraria from Accuray outside of the submitted work. All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Windisch, P., Weber, P., Fürweger, C. et al. Implementation of model explainability for a basic brain tumor detection using convolutional neural networks on MRI slices. Neuroradiology 62, 1515–1518 (2020).

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  • Deep learning
  • Explainability
  • Machine learning
  • Artificial intelligence
  • Gliobastoma
  • Vestibular Schwannoma