A text-guided CLIP-style model with a frozen BERT text encoder and cross-entropy alignment classifies focal liver lesions from multi-phase CT slices with about 79 percent average accuracy, outperforming CLIP and MedCLIP on a small in-house dataset.
al.: Case Discrimination: Self-supervised Feature Learning for the classifica- tion of Focal Liver Lesions,” in Chen, Y
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A Vision-Language Model for Focal Liver Lesion Classification
A text-guided CLIP-style model with a frozen BERT text encoder and cross-entropy alignment classifies focal liver lesions from multi-phase CT slices with about 79 percent average accuracy, outperforming CLIP and MedCLIP on a small in-house dataset.