Public articles linked to the same research event.
arXiv The work proposes Dino U-Net: a frozen DINOv3 foundation backbone as encoder, combined with a dual-branch DINO Adapter and a Fidelity-Aware Projection Module (FAPM), which outperforms seven baseline methods on seven public medical image datasets spanning endoscopy, ultrasound, microscopy, MRI, fundus and CMR modalities, and shows performance improving as the backbone scales from S to 7B.
The work proposes Dino U-Net: a frozen DINOv3 foundation backbone as encoder, combined with a dual-branch DINO Adapter and a Fidelity-Aware Projection Module (FAPM), which outperforms seven baseline methods on seven public medical image datasets spanning endoscopy, ultrasound, microscopy, MRI, fundus and CMR modalities, and shows performance improving as the backbone scales from S to 7B.
The work proposes Dino U-Net: a frozen DINOv3 foundation backbone as encoder, combined with a dual-branch DINO Adapter and a Fidelity-Aware Projection Module (FAPM), which outperforms seven baseline methods on seven public medical image datasets spanning endoscopy, ultrasound, microscopy, MRI, fundus and CMR modalities, and shows performance improving as the backbone scales from S to 7B.
The work proposes Dino U-Net: a frozen DINOv3 foundation backbone as encoder, combined with a dual-branch DINO Adapter and a Fidelity-Aware Projection Module (FAPM), which outperforms seven baseline methods on seven public medical image datasets spanning endoscopy, ultrasound, microscopy, MRI, fundus and CMR modalities, and shows performance improving as the backbone scales from S to 7B.