Multi-GranularityCross-modalAlignmentfor GeneralizedMedicalVisualRepresentationLearning (SupplementaryMaterial)

We use the open-source mimic-cxr repository4 to extract impression and findings for each report. Following [9], we pick out sequences of alphanumeric characters and drop all other characters and symbols for all reports, and remove reports which contain less than3 tokens. Following common practice in ViT [5], we split the radiograph with patch size16 16,which results in 196 visual tokens for each image. The instance-level projection layer is a two-layer MultiLayer Perceptron (MLP) with Batch Normalization [10] and ReLU activation function. Additionally, we use a frozen Batch Normalization layer after the MLP toobtain instance-levelembeddings.