June 11, 2024, 4:42 a.m. | Sajid Javed, Arif Mahmood, Iyyakutti Iyappan Ganapathi, Fayaz Ali Dharejo, Naoufel Werghi, Mohammed Bennamoun

cs.CL updates on arXiv.org arxiv.org

arXiv:2406.05205v1 Announce Type: cross
Abstract: This paper proposes Comprehensive Pathology Language Image Pre-training (CPLIP), a new unsupervised technique designed to enhance the alignment of images and text in histopathology for tasks such as classification and segmentation. This methodology enriches vision-language models by leveraging extensive data without needing ground truth annotations. CPLIP involves constructing a pathology-specific dictionary, generating textual descriptions for images using language models, and retrieving relevant images for each text snippet via a pre-trained model. The model is then …

alignment arxiv cs.cl cs.cv cs.lg cs.mm eess.iv language type vision vision-language zero-shot

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