MoCo Pretraining Improves Representation and Transferability of Chest X-ray Models

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Contrastive Learning of Medical Visual Representations from Paired Images and Text (2020, background): “Our work is inspired by the recent line of work on image view-based contrastive learning (Hénaff et al., 2020; Chen et al., 2020a; He et al., 2020; Grill et al., 2020; Sowrirajan et al., 2021; Azizi et al., 2021), but differs from existing studies by the contrastive learning with text modality, whic”
AUC Maximization in the Era of Big Data and AI: A Survey (2022, unknown): “This approach was recently explored in [4, 151, 199].”
MedAug: Contrastive learning leveraging patient metadata improves representations for chest X-ray interpretation (2021, methodology): “Despite the early success of these applications, there is limited work on determining how to improve upon standard contrastive algorithms using medical information (Sowrirajan et al., 2020; Chai-tanya et al., 2020; Zhang et al., 2020; Kiyasseh et al., 2020).”
Big Self-Supervised Models Advance Medical Image Classification (2021, methodology): “[39] explore the use of MoCo pretraining for classification of CheXpert dataset through linear evaluation.”