Abstract
Purpose
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.
Methods
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.
Results
Making the decisions of a network more explainable helped to identify potential bias and choose appropriate training data.
Conclusion
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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
El Jundi B, Heinze S, Lenschow C et al (2009) The locust standard brain: a 3D standard of the central complex as a platform for neural network analysis. Front Syst Neurosci 3:21
Zhou Z, Sanders JW, Johnson JM et al (2020) Computer-aided detection of brain metastases in T1-weighted MRI for stereotactic radiosurgery using deep learning single-shot detectors. Radiology:295(2):407–415
Kickingereder P, Isensee F, Tursunova I, Petersen J, Neuberger U, Bonekamp D, Brugnara G, Schell M, Kessler T, Foltyn M, Harting I, Sahm F, Prager M, Nowosielski M, Wick A, Nolden M, Radbruch A, Debus J, Schlemmer HP, Heiland S, Platten M, von Deimling A, van den Bent MJ, Gorlia T, Wick W, Bendszus M, Maier-Hein KH (2019) Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study. Lancet Oncol 20:728–740
Jang B-S, Jeon SH, Kim IH, Kim IA (2018) Prediction of pseudoprogression versus progression using machine learning algorithm in glioblastoma. Sci Rep 8:12516
Castelvecchi D (2016) Can we open the black box of AI? Nature 538:20–23
Selvaraju RR, Cogswell M, Das A, et al (2016) Grad-CAM: visual explanations from deep networks via gradient-based localization. arXiv [cs.CV]
Gal Y, Ghahramani Z (2015) Dropout as a Bayesian approximation: representing model uncertainty in deep learning. arXiv [statML]
He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. arXiv [cs.CV]
Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, Ohgaki H, Wiestler OD, Kleihues P, Ellison DW (2016) The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131:803–820
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F (2013) The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26:1045–1057
Code availability
The trained model can be provided by the corresponding author.
The Bayesian neural network was implemented based on https://github.com/DanyWind/fastai_bayesian.
The Grad-CAM implementation can be found at https://github.com/anhquan0412/animation-classification/blob/master/gradcam.py
The pretrained resnet50 was imported from the fastai library.
The IXI dataset can be downloaded from https://brain-development.org/ixi-dataset/.
The glioblastoma dataset can be downloaded from https://wiki.cancerimagingarchive.net/display/Public/TCGA-GBM#715bed1a14224923b50f1f2e7dae54a1.
Funding
No funding was received for this project.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
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.
Ethical approval
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.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
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). https://doi.org/10.1007/s00234-020-02465-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00234-020-02465-1