arXiv Computer Vision
One-Shot Adaptive Segmentation For Scientific Images
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ESRVS: Extreme Semi-Supervised Retinal Vessel Segmentation with a Single Annotated Image
arXiv:2607. 24453v1 Announce Type: cross Abstract: Learning from minimal human supervision is a long-standing goal in medical image analysis, where dense expert annotations are costly.
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SegDINO: Introducing Multi-Scale Structure into DINO for Efficient Medical Image Segmentation
arXiv:2606. 17972v1 Announce Type: cross Abstract: Self-supervised DINO models provide strong transferable visual representations, yet applying them directly to image segmentation remains challenging.
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Learning Where to Focus: Self-Supervised Multi-Scale ViTs for Histopathology
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Example-based Robust Abnormality Detection with Minimal Annotations using Exemplar Med-DETR
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Exemplar: Classical Priors Complement Frozen Features for Few-Shot Microscopy Segmentation at Native Resolution
Exemplar is a few‑shot segmentation method that combines a frozen DINOv3 backbone with a fixed bank of classical native‑resolution filter responses in a single lightweight head. Trained only from support masks, it achieves a mean foreground intersection‑over‑union of 0.782 across eleven biomedical imaging datasets, outperforming either component alone and surpassing five other few‑shot methods in 54 of 55 comparisons. With a single annotated mask, Exemplar reaches 0.703, higher than a from‑scratch nnU‑Net trained on the same mask, and while nnU‑Net eventually overtakes it with eight masks, it requires 16–77× longer to fit.