Search Results for author:
Found 3 papers, 3 papers with code
Less Could Be Better: Parameter-efficient Fine-tuning Advances Medical Vision Foundation Models
Parameter-efficient fine-tuning (PEFT) that was initially developed for exploiting pre-trained large language models has recently emerged as an effective approach to perform transfer learning on computer vision tasks.
Advancing Radiograph Representation Learning with Masked Record Modeling
Modern studies in radiograph representation learning rely on either self-supervision to encode invariant semantics or associated radiology reports to incorporate medical expertise, while the complementarity between them is barely noticed.
Breaking the Dilemma of Medical Image-to-image Translation
In order to break the dilemma of the existing modes, we propose a new unsupervised mode called RegGAN for medical image-to-image translation.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
