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We compare strategies for selecting positive pairs for chest X-ray interpretation including requiring them to be from the same patient, imaging study or laterality. ... In addition, we explore leveraging patient metadata to select hard negative pairs for contrastive learning, but do not find improvement over baselines that do not use metadata. ... Discussion We introduce MedAug, a method to use patient metadata to select positive pairs for contrastive learning, and demonstrate the utility of this method on a chest X-ray interpretation task. ...arXiv:2102.10663v2 fatcat:fedyfy4hu5gxbfvy46x4no7fai
In this work, we use lung segmentation in chest X-rays as a case study and propose a contrastive learning framework with temporal correlated medical images, named CL-TCI, to learn superior encoders for ... Contrastive learning has been proved to be a promising technique for image-level representation learning from unlabeled data. ... All chest radiographs for a series of consecutive cardiac patients receiving a chest radiograph in June 2021 were identified, anonymized, and exported for review. ...arXiv:2109.03233v2 fatcat:h7gdikhrgbcjhnxp2o6qcuurpe
This work will enable researchers and clinicians to understand the topography of the domain, describe the state-of-the-art, and detect research gaps for future research in multimodal medical machine learning ... This becomes particularly apparent within the field of radiology, which, due to its information density, accessibility, and computational interpretability, constitutes a central pillar in the healthcare ... Similar to  ,  developed MedAug, a method that uses patient metadata for the selection of positive pairs. ...doi:10.5167/uzh-219067 fatcat:iy5sdnk7bnad3dprmpstrtz5iu