Parameter Efficient Fine-tuning for Domain-specific Gastrointestinal Disease Recognition

ArXi:2604.10451v1 Announce Type: new Despite recent advancements in the field of medical image analysis with the use of pretrained foundation models, the issue of distribution shifts between cross-source images largely remains adamant. To circumvent that issue, investigators generally train a separate model for each source. However, this method becomes expensive when we fully fine-tune pretrained large models for a single dataset, as we must multiple copies of those models.