One-shot Localization and Segmentation of Medical Images with Foundation Models

doi:doi:10.57702/40tzo4ib

One-shot Localization and Segmentation of Medical Images with Foundation Models

A variety of pre-trained ViT (DINO, DINOv2, SAM, CLIP) and SD models, trained exclusively on natural images, for solving the correspondence problems on medical images.

BibTex:

@dataset{Deepa_Anand_and_Gurunath_Reddy_M_and_Vanika_Singhal_and_Dattesh_D_Shanbhag_and_Shriram_KS_and_Uday_Patil_and_Chitresh_Bhushan_and_Kavitha_Manickam_and_Dawei_Gui_and_Rakesh_Mullick_and_Avinash_Gopal_and_Parminder_Bhatia_and_Taha_Kass-Hout_2024,
    abstract = {A variety of pre-trained ViT (DINO, DINOv2, SAM, CLIP) and SD models, trained exclusively on natural images, for solving the correspondence problems on medical images.},
    author = {Deepa Anand and Gurunath Reddy M and Vanika Singhal and Dattesh D. Shanbhag and Shriram KS and Uday Patil and Chitresh Bhushan and Kavitha Manickam and Dawei Gui and Rakesh Mullick and Avinash Gopal and Parminder Bhatia and Taha Kass-Hout},
    doi = {10.57702/40tzo4ib},
    institution = {No Organization},
    keyword = {'Correspondence Problems', 'Medical Images', 'SD Models', 'ViT'},
    month = {dec},
    publisher = {TIB},
    title = {One-shot Localization and Segmentation of Medical Images with Foundation Models},
    url = {https://service.tib.eu/ldmservice/dataset/one-shot-localization-and-segmentation-of-medical-images-with-foundation-models},
    year = {2024}
}