mlsanityData to 7 Oct 2026

Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity

arXiv 2305.08252 ↗First posted 14 May 2023cs.CV cs.AIcited by 54

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Abstract (arXiv)

Foundation models have significantly advanced medical image analysis through the pre-train fine-tune paradigm. Among various fine-tuning algorithms, Parameter-Efficient Fine-Tuning (PEFT) is increasingly utilized for knowledge transfer across diverse tasks, including vision-language and text-to-image generation. However, its application in medical image analysis is relatively unexplored due to the lack of a structured benchmark for evaluating PEFT methods. This study fills this gap by evaluating 17 distinct PEFT algorithms across convolutional and transformer-based networks on image classification and text-to-image generation tasks using six medical datasets of varying size, modality, and complexity. Through a battery of over 700 controlled experiments, our findings demonstrate PEFT's effectiveness, particularly in low data regimes common in medical imaging, with performance gains of up to 22% in discriminative and generative tasks. These recommendations can assist the community in incorporating PEFT into their workflows and facilitate fair comparisons of future PEFT methods, ensuring alignment with advancements in other areas of machine learning and AI.

Key facts. Cited by 54 papers in the map (data through 7 Oct 2026). Builds on 58 of its 97 references that are in the map. First posted on arXiv 14 May 2023.

Builds on

58 of 97 references.

References · 58

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 22; 2025: 19; 2026: 8.

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  1. 2023Improving Tuning-Free Real Image Editing with Proximal Guidancecited by 87
  2. 2023Artificial General Intelligence for Medical Imaging Analysiscited by 32
  3. 2024How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Modelcited by 32
  4. 2023Evaluating General Purpose Vision Foundation Models for Medical Image Analysis: An Experimental Study of DINOv2 on Radiology Benchmarkscited by 30
  5. 2024Data-Centric Foundation Models in Computational Healthcare: A Surveycited by 27
  6. 2024Parameter Efficient Fine Tuning: A Comprehensive Analysis Across Applicationscited by 25
  7. 2023SAM-PARSER: Fine-tuning SAM Efficiently by Parameter Space Reconstructioncited by 23
  8. 2023FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysiscited by 23
  9. 2024Low-Rank Adaptation of Time Series Foundational Models for Out-of-Domain Modality Forecastingcited by 18
  10. 2024Less Could Be Better: Parameter-efficient Fine-tuning Advances Medical Vision Foundation Modelscited by 17
  11. 2025PRISM: High-Resolution & Precise Counterfactual Medical Image Generation using Language-guided Stable Diffusioncited by 17
  12. 2024ConvLoRA and AdaBN based Domain Adaptation via Self-Trainingcited by 10
38 more
  1. 2024Beyond LoRA: Exploring Efficient Fine-Tuning Techniques for Time Series Foundational Modelscited by 10
  2. 2024Fine-grained Prompt Tuning: A Parameter and Memory Efficient Transfer Learning Method for High-resolution Medical Image Classificationcited by 7
  3. 2024Embedded Visual Prompt Tuningcited by 7
  4. 2025Foundation Models in Medical Imaging: A Review and Outlookcited by 7
  5. 2024BMFT: Achieving Fairness via Bias-based Weight Masking Fine-tuningcited by 6
  6. 2024LoRTA: Low Rank Tensor Adaptation of Large Language Modelscited by 5
  7. 2025CheXGenBench: A Unified Benchmark For Fidelity, Privacy and Utility of Synthetic Chest Radiographscited by 5
  8. 2025Efficient Medical Vision-Language Alignment Through Adapting Masked Vision Modelscited by 5
  9. 2024Sparsity- and Hybridity-Inspired Visual Parameter-Efficient Fine-Tuning for Medical Diagnosiscited by 3
  10. 2024MemControl: Mitigating Memorization in Diffusion Models via Automated Parameter Selectioncited by 3
  11. 2024Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noisecited by 3
  12. 2025Exploiting Mixture-of-Experts Redundancy Unlocks Multimodal Generative Abilitiescited by 3
  13. 2025Benchmarking Foundation Models and Parameter-Efficient Fine-Tuning for Prognosis Prediction in Medical Imagingcited by 3
  14. 2025RAU: Reference-based Anatomical Understanding with Vision Language Modelscited by 3
  15. 2025Adaptation of Foundation Models for Medical Image Analysis: Strategies, Challenges, and Future Directionscited by 3
  16. 2026SkinCLIP-VL: Consistency-Aware Vision-Language Learning for Multimodal Skin Cancer Diagnosiscited by 3
  17. 2024Boosting Memory Efficiency in Transfer Learning for High-Resolution Medical Image Classificationcited by 2
  18. 2024SeLoRA: Self-Expanding Low-Rank Adaptation of Latent Diffusion Model for Medical Image Synthesiscited by 2
  19. 2024Capacity Control is an Effective Memorization Mitigation Mechanism in Text-Conditional Diffusion Modelscited by 2
  20. 2025The Devil is in the Prompts: De-Identification Traces Enhance Memorization Risks in Synthetic Chest X-Ray Generationcited by 2
  21. 2025Pre-trained Models Succeed in Medical Imaging with Representation Similarity Degradationcited by 2
  22. 2025BleedOrigin: Dynamic Bleeding Source Localization in Endoscopic Submucosal Dissection via Dual-Stage Detection and Trackingcited by 2
  23. 2025How to make Medical AI Systems safer? Simulating Vulnerabilities, and Threats in Multimodal Medical RAG Systemcited by 2
  24. 2024Parameter Efficient Fine Tuning for Multi-scanner PET to PET Reconstructioncited by 1
  25. 2024Pre-training Everywhere: Parameter-Efficient Fine-Tuning for Medical Image Analysis via Target Parameter Pre-trainingcited by 1
  26. 2024SURE-VQA: Systematic Understanding of Robustness Evaluation in Medical VQA Taskscited by 1
  27. 2024LQ-Adapter: ViT-Adapter with Learnable Queries for Gallbladder Cancer Detection from Ultrasound Imagecited by 1
  28. 2025Parameter Efficient Fine-Tuning of Segment Anything Model for Biomedical Imagingcited by 1
  29. 2025COLORA: Efficient Fine-Tuning for Convolutional Models with a Study Case on Optical Coherence Tomography Image Classificationcited by 1
  30. 2025WaRA: Wavelet Low-Rank Adaptation for Medical Image Classificationcited by 1
  31. 2025Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentationcited by 1
  32. 2025Federated Fine-tuning of SAM-Med3D for MRI-based Dementia Classificationcited by 1
  33. 2026SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentationcited by 1
  34. 2026When Adaptation Hurts: Connecting Representational Drift to OOD Failures in MedSAM Fine-Tuningcited by 1
  35. 2024A Novel Hybrid Parameter-Efficient Fine-Tuning Approach for Hippocampus Segmentation and Alzheimer's Disease Diagnosiscited by 0
  36. 2025Efficient Self-Supervised Adaptation for Medical Image Analysiscited by 0
  37. 2025Efficient Parameter Adaptation for Multi-Modal Medical Image Segmentation and Prognosiscited by 0
  38. 20261%>100%: High-Efficiency Visual Adapter with Complex Linear Projection Optimizationcited by 0

50 most cited of 54 All 54 (JSON)