# DINOv2 based Self Supervised Learning For Few Shot Medical Image Segmentation

arXiv: https://arxiv.org/abs/2403.03273 · HTML: https://mlsanity.com/paper/2403.03273/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 5 Mar 2024.

## Builds on (most cited first, 10 of 10)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment](https://mlsanity.com/paper/1908.06391/) (2019)
- [CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation](https://mlsanity.com/paper/2001.06535/) (2020)
- [Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation](https://mlsanity.com/paper/2007.09886/) (2020)
- ['Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images](https://mlsanity.com/paper/1902.01314/) (2019)
- [Few-shot Medical Image Segmentation with Cycle-resemblance Attention](https://mlsanity.com/paper/2212.03967/) (2022)

## Built on by (most cited first, 11 of 11)

- [SegDINO: An Efficient Design for Medical and Natural Image Segmentation with DINO-V3](https://mlsanity.com/paper/2509.00833/) (2025)
- [ProtoSAM: One-Shot Medical Image Segmentation With Foundational Models](https://mlsanity.com/paper/2407.07042/) (2024)
- [ComFe: An Interpretable Head for Vision Transformers](https://mlsanity.com/paper/2403.04125/) (2024)
- [Anatomy-Aware Low-Dose CT Denoising via Pretrained Vision Models and Semantic-Guided Contrastive Learning](https://mlsanity.com/paper/2508.07788/) (2025)
- [GRPO-RM: Fine-Tuning Representation Models via GRPO-Driven Reinforcement Learning](https://mlsanity.com/paper/2511.15256/) (2025)
- [Interpreting the structure of multi-object representations in vision encoders](https://mlsanity.com/paper/2406.09067/) (2024)
- [TomoGraphView: 3D Medical Image Classification with Omnidirectional Slice Representations and Graph Neural Networks](https://mlsanity.com/paper/2511.09605/) (2025)
- [Learning Generalizable 3D Medical Image Representations from Mask-Guided Self-Supervision](https://mlsanity.com/paper/2603.13660/) (2026)
- [IConE: Batch Independent Collapse Prevention for Self-Supervised Representation Learning](https://mlsanity.com/paper/2603.15263/) (2026)
- [SegDINO: Introducing Multi-Scale Structure into DINO for Efficient Medical Image Segmentation](https://mlsanity.com/paper/2606.17972/) (2026)
- [TextDS: Parameter-Efficient Representation Alignment for Scene Text Detection under Distribution Shifts](https://mlsanity.com/paper/2606.28077/) (2026)

## Abstract (arXiv)

Deep learning models have emerged as the cornerstone of medical image segmentation, but their efficacy hinges on the availability of extensive manually labeled datasets and their adaptability to unforeseen categories remains a challenge. Few-shot segmentation (FSS) offers a promising solution by endowing models with the capacity to learn novel classes from limited labeled examples. A leading method for FSS is ALPNet, which compares features between the query image and the few available support segmented images. A key question about using ALPNet is how to design its features. In this work, we delve into the potential of using features from DINOv2, which is a foundational self-supervised learning model in computer vision. Leveraging the strengths of ALPNet and harnessing the feature extraction capabilities of DINOv2, we present a novel approach to few-shot segmentation that not only enhances performance but also paves the way for more robust and adaptable medical image analysis.