A biomedical vision-language model is pre-trained with a contrastive loss that distinguishes original radiology reports from nine perturbed variants, plus a local attention loss, and is reported to beat ConVIRT and GLoRIA on some downstream benchmarks.
Scaling up visual and vision-language representation learning with noisy text supervision,
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Enhancing Biomedical Multi-modal Representation Learning with Multi-scale Pre-training and Perturbed Report Discrimination
A biomedical vision-language model is pre-trained with a contrastive loss that distinguishes original radiology reports from nine perturbed variants, plus a local attention loss, and is reported to beat ConVIRT and GLoRIA on some downstream benchmarks.