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Noisy data present in medical imaging datasets can often aid the developmentof robust models that are equipped to handle real-world data. However, if thebad data contains insufficient anatomical information, it can have a severenegative effect on the model's performance. We propose a novel methodologyusing a semi-supervised Siamese network to identify bad data. This methodrequires only a small pool of 'reference' medical images to be reviewed by anon-expert human to ensure the major anatomical structures are present in theField of View. The model trains on this reference set and identifies bad databy using the Siamese network to compute the distance between the reference setand all other medical images in the dataset. This methodology achieves an AreaUnder the Curve (AUC) of 0.989 for identifying bad data. Code will be availableat https://git.io/JYFuV.

Semi-Supervised Siamese Network for Identifying Bad Data in Medical Imaging Datasets