Jul 2026· Signal Processing and Communications Applications Conference· pp. 1-4· 0 citations· 41 references
Abstract
Vision-language models (VLMs) have significantly advanced open-vocabulary image understanding by learning aligned representations from large-scale image-text datasets. Despite their zero-shot generalization capabilities, adapting these foundation models to specific application domains remains challenging. Full fine-tuning is often infeasible due to computational costs and the risk of overfitting when labeled data are limited. Parameter-efficient fine-tuning (PEFT) approaches promise to address this issue by updating only a small set of parameters while keeping the pre-trained encoders largely frozen. However, existing PEFT strategies often exhibit insufficient spatial awareness, rendering them suboptimal for medical imaging, where subtle visual differences can lead to distinct clinical diagnoses. To address this challenge, in this study we propose a novel PEFT technique that leverages natural-image pretrained DINOv3’s attention maps to enforce spatial alignment.
This survey comprehensively evaluates the underlying mechanisms, inherent strengths, and specific weaknesses of each FSIC methods into three categories: Metric Learning, Optimization/Meta-Learning, and Transfer Learning with Large Model Fine-Tuning.
A clear, reproducible characterization of how frozen medical encoders represent findings is provided, demonstrating direct applicability across models and outperforms published CT-CHAT on clinical efficacy and NLG metrics.
F. Nooralahzadeh, L. Bogensperger, C. Bluethgen et al.· arXiv.org· 0 citations
Dual-Level Contrastive Learning (DLCL) is proposed, the first prompt-based VLM framework for out-of-distribution (OOD) detection in FCU, to the best of the authors' knowledge and significantly outperforming state-of-the-art methods.
Zi-Yi Liu, Wei-Hu Song, Yu-Peng Ma et al.· Proceedings of the Thirty-Fi...· 0 citations
This work systematically examines how MedSAM generalizes across diverse medical imaging benchmarks, with six adaptation strategies: full-model and encoder-only LoRA, shallow and deep visual prompt tuning (VPT), and decoder-only and full fine-tuning, and concludes that robust MedSAM adaptation requires the combined cons...
Marko Haralović, Sounic Akkaraju, Carlo Baretta et al.· 1 citation· ⚡1
TopKSigLIP outperforms existing open-source mammography and general medical VLMs on both internal and external benchmarks on density assessment, BI-RADS classification, finding subtyping, and cancer prediction under zero-shot evaluation.
Y. Jeon, Beatrice Brown-Mulry, R. Isaac et al.· 0 citations
Medical image segmentation is a key component of computer-aided diagnosis and treatment planning. Despite substantial progress in deep learning–based models, most existing approaches depend heavily on large annotated datasets and often fail to generalize across heterogeneous clinical environments, limiting their deploy...
V. Nguyen, Hoang Quan Luong, Phuc Ngoc Pham· IEEE International Conferenc...· 0 citations
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