Implementation of model explainability for a basic brain tumor detection using convolutional neural networks on MRI slices
- 4 June 2020
- journal article
- research article
- Published by Springer Science and Business Media LLC in Neuroradiology
- Vol. 62 (11), 1515-1518
- https://doi.org/10.1007/s00234-020-02465-1
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.Keywords
This publication has 9 references indexed in Scilit:
- Computer-aided Detection of Brain Metastases in T1-weighted MRI for Stereotactic Radiosurgery Using Deep Learning Single-Shot DetectorsRadiology, 2020
- Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective studyThe Lancet Oncology, 2019
- Prediction of Pseudoprogression versus Progression using Machine Learning Algorithm in GlioblastomaScientific Reports, 2018
- Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based LocalizationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2017
- Can we open the black box of AI?Nature, 2016
- Deep Residual Learning for Image RecognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summaryActa Neuropathologica, 2016
- The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information RepositoryJournal of Digital Imaging, 2013
- The locust standard brain: a 3D standard of the central complex as a platform for neural network analysisFrontiers in Systems Neuroscience, 2009