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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    References

    SHOWING 1-10 OF 21 REFERENCES
    Anomaly Detection Using Deep Learning Based Image Completion
    • 27
    • PDF
    Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
    • 707
    • PDF
    Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection
    • 51
    • Highly Influential
    • PDF
    U-Net: Convolutional Networks for Biomedical Image Segmentation
    • 19,046
    • PDF
    Reliable object detection and segmentation using inpainting
    • 3
    • PDF
    Unsupervised Learning of Anomaly Detection from Contaminated Image Data using Simultaneous Encoder Training
    • 8
    • PDF
    Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation
    • 304
    • PDF
    GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
    • 204
    • PDF
    The preprocessed connectomes project repository of manually corrected skull-stripped T1-weighted anatomical MRI data
    • 15
    • PDF
    Magnetic Resonance Image Tissue Classification Using a Partial Volume Model
    • 962
    • PDF