mlsanityData to 7 Oct 2026

ProtoSAM: One-Shot Medical Image Segmentation With Foundational Models

arXiv 2407.07042 ↗First posted 9 Jul 2024cs.CV cs.AIcited by 12

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Abstract (arXiv)

This work introduces a new framework, ProtoSAM, for one-shot medical image segmentation. It combines the use of prototypical networks, known for few-shot segmentation, with SAM - a natural image foundation model. The method proposed creates an initial coarse segmentation mask using the ALPnet prototypical network, augmented with a DINOv2 encoder. Following the extraction of an initial mask, prompts are extracted, such as points and bounding boxes, which are then input into the Segment Anything Model (SAM). State-of-the-art results are shown on several medical image datasets and demonstrate automated segmentation capabilities using a single image example (one shot) with no need for fine-tuning of the foundation model. Our code is available at: https://github.com/levayz/ProtoSAM

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 15 of its 25 references that are in the map. First posted on arXiv 9 Jul 2024.

Builds on

15 of 25 references.

References · 15

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  3. 2023Segment Anythingcited by 8,158
  4. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,764
  5. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  6. 2023Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentationcited by 485
  7. 2019PANet: Few-Shot Image Semantic Segmentation with Prototype Alignmentcited by 449
  8. 2020CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentationcited by 307
  9. 2020Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotationcited by 128
  10. 2024Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directionscited by 97
  11. 2023AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encodercited by 70
  12. 2019'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Imagescited by 69
3 more
  1. 2020Semi-supervised few-shot learning for medical image segmentationcited by 34
  2. 2022Few-shot Medical Image Segmentation with Cycle-resemblance Attentioncited by 33
  3. 2024DINOv2 based Self Supervised Learning For Few Shot Medical Image Segmentationcited by 11

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 5; 2026: 5.

12 papers.

  1. 2024MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentationcited by 37
  2. 2025SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Promptscited by 9
  3. 2025Prompt learning with bounding box constraints for medical image segmentationcited by 2
  4. 2025Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentationcited by 1
  5. 2025DescriptorMedSAM: Language-Image Fusion with Multi-Aspect Text Guidance for Medical Image Segmentationcited by 1
  6. 2026Focus on Background: Exploring SAM's Potential in Few-shot Medical Image Segmentation with Background-centric Promptingcited by 1
  7. 2026Mask to Concept: Auto-Promptable SAM3 via Efficient Test-Time Concept Embedding Search for Few-Shot Annotationcited by 1
  8. 2026Prototype Memory-Guided Training-Free Anomaly Classification and Localization in Prenatal Ultrasoundcited by 1
  9. 2024Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domaincited by 0
  10. 2025Optimization of MedSAM model based on bounding box adaptive perturbation algorithmcited by 0
  11. 2026DiffuSAM: Diffusion-Based Prompt-Free SAM2 for Few-Shot and Source-Free Medical Image Segmentationcited by 0
  12. 2026Memory-Supported Synergistic Adaptation for Training-Free Test-Time Medical Image Segmentationcited by 0