Addressing The False Negative Problem of MRI Reconstruction Networks by Adversarial Attacks and Robust TrainingDownload PDF

25 Jan 2020 (modified: 27 Jun 2020)MIDL 2020 Conference Blind SubmissionReaders: Everyone
  • Keywords: MRI Reconstruction, Adversarial Attack, Robust Training
  • Track: full conference paper
  • Abstract: Deep learning models have been shown to be successful in accelerating MRI reconstruction, over traditional methods. However, it has been observed that these methods tend to miss rare small features, such as meniscal tears, subchondral osteophyte, etc. in musculoskeletal applications. This is a concerning finding as these small and rare features are the particularly relevant in clinical diagnostic settings. Additionally, such potentially dangerous loss of details in the reconstructed images are not reflected by global image fidelity metrics such as mean-square error (MSE) and structural similarity metric (SSIM). In this work, we propose a framework to find the worst-case false negatives by adversarially attacking the trained models and improve the models' ability to reconstruct the small features by robust training.
  • Paper Type: methodological development
  • TL;DR: We propose a framework to find the worst-case false negatives by adversarially attacking the trained models and improve the models' ability to reconstruct the small features by robust training.
  • Source Latex: zip
  • Presentation Upload: zip
  • Presentation Upload Agreement: I agree that my presentation material (videos and slides) will be made public.
11 Replies

Loading