Corpus ID: 222134193

Unsupervised Region-based Anomaly Detection in Brain MRI with Adversarial Image Inpainting

@article{Nguyen2020UnsupervisedRA,
  title={Unsupervised Region-based Anomaly Detection in Brain MRI with Adversarial Image Inpainting},
  author={B. Nguyen and A. Feldman and S. Bethapudi and A. Jennings and Chris G. Willcocks},
  journal={ArXiv},
  year={2020},
  volume={abs/2010.01942}
}
  • B. Nguyen, A. Feldman, +2 authors Chris G. Willcocks
  • Published 2020
  • Computer Science, Engineering
  • ArXiv
  • Medical segmentation is performed to determine the bounds of regions of interest (ROI) prior to surgery. By allowing the study of growth, structure, and behaviour of the ROI in the planning phase, critical information can be obtained, increasing the likelihood of a successful operation. Usually, segmentations are performed manually or via machine learning methods trained on manual annotations. In contrast, this paper proposes a fully automatic, unsupervised inpainting-based brain tumour… CONTINUE READING

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