# EasyLens: A Training-Free Plug-and-Play Subtle-Lesion Representation Amplifier for Medical Vision-Language Models

arXiv: https://arxiv.org/abs/2606.06379 · HTML: https://mlsanity.com/paper/2606.06379/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 28 of its 33 references that are in the map.
- First posted on arXiv 4 Jun 2026.

## Placed in

- Multimodal LLMs / vision-language models / [Medical vision-language models](https://mlsanity.com/mllm/medical-vision-language-models/)

## Builds on (most cited first, 28 of 28)

- [Contrastive Learning of Medical Visual Representations from Paired Images and Text](https://mlsanity.com/paper/2010.00747/) (2020)
- [MedCLIP: Contrastive Learning from Unpaired Medical Images and Text](https://mlsanity.com/paper/2210.10163/) (2022)
- [MedGemma Technical Report](https://mlsanity.com/paper/2507.05201/) (2025)
- [Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing](https://mlsanity.com/paper/2204.09817/) (2022)
- [MedKLIP: Medical Knowledge Enhanced Language-Image Pre-Training in Radiology](https://mlsanity.com/paper/2301.02228/) (2023)
- [Knowledge-enhanced Visual-Language Pre-training on Chest Radiology Images](https://mlsanity.com/paper/2302.14042/) (2023)
- [Vision-Language Models for Medical Report Generation and Visual Question Answering: A Review](https://mlsanity.com/paper/2403.02469/) (2024)
- [MedRAX: Medical Reasoning Agent for Chest X-ray](https://mlsanity.com/paper/2502.02673/) (2025)
- [Improving Medical Large Vision-Language Models with Abnormal-Aware Feedback](https://mlsanity.com/paper/2501.01377/) (2025)
- [ChestX-Reasoner: Advancing Radiology Foundation Models with Reasoning through Step-by-Step Verification](https://mlsanity.com/paper/2504.20930/) (2025)
- [MultiMedEval: A Benchmark and a Toolkit for Evaluating Medical Vision-Language Models](https://mlsanity.com/paper/2402.09262/) (2024)
- [VividMed: Vision Language Model with Versatile Visual Grounding for Medicine](https://mlsanity.com/paper/2410.12694/) (2024)
- [Large Language Model with Region-guided Referring and Grounding for CT Report Generation](https://mlsanity.com/paper/2411.15539/) (2024)
- [Fine-Grained Image-Text Alignment in Medical Imaging Enables Explainable Cyclic Image-Report Generation](https://mlsanity.com/paper/2312.08078/) (2023)
- [MMedPO: Aligning Medical Vision-Language Models with Clinical-Aware Multimodal Preference Optimization](https://mlsanity.com/paper/2412.06141/) (2024)
- [Better Tokens for Better 3D: Advancing Vision-Language Modeling in 3D Medical Imaging](https://mlsanity.com/paper/2510.20639/) (2025)
- [Anatomical Structure-Guided Medical Vision-Language Pre-training](https://mlsanity.com/paper/2403.09294/) (2024)
- [MIMO: A medical vision language model with visual referring multimodal input and pixel grounding multimodal output](https://mlsanity.com/paper/2510.10011/) (2025)
- [Argus: Benchmarking and Enhancing Vision-Language Models for 3D Radiology Report Generation](https://mlsanity.com/paper/2406.07146/) (2024)
- [AOR: Anatomical Ontology-Guided Reasoning for Medical Large Multimodal Model in Chest X-Ray Interpretation](https://mlsanity.com/paper/2505.02830/) (2025)
- [MLIP: Medical Language-Image Pre-training with Masked Local Representation Learning](https://mlsanity.com/paper/2401.01591/) (2024)
- [Photon: Speedup Volume Understanding with Efficient Multimodal Large Language Models](https://mlsanity.com/paper/2603.25155/) (2026)
- [A Reasoning-Enabled Vision-Language Foundation Model for Chest X-ray Interpretation](https://mlsanity.com/paper/2604.00493/) (2026)
- [RadZero: Similarity-Based Cross-Attention for Explainable Vision-Language Alignment in Chest X-ray with Zero-Shot Multi-Task Capability](https://mlsanity.com/paper/2504.07416/) (2025)
- [TotalFM: An Organ-Separated 3D-CT Foundation Model Leveraging Large-Scale Routine Clinical Radiology Data](https://mlsanity.com/paper/2601.00260/) (2026)
- [Improving Medical Visual Representation Learning with Pathological-level Cross-Modal Alignment and Correlation Exploration](https://mlsanity.com/paper/2506.10573/) (2025)
- [Seeing Like Radiologists: Context- and Gaze-Guided Vision-Language Pretraining for Chest X-rays](https://mlsanity.com/paper/2603.26049/) (2026)
- [Seeing Through Experts Eyes A Foundational Vision Language Model Trained on Radiologists Gaze and Reasoning](https://mlsanity.com/paper/2604.14316/) (2026)

## Abstract (arXiv)

Medical vision-language models (VLMs) have shown increasing potential for clinical image interpretation, including lesion detection and report generation. However, their practical utility remains limited by insufficient sensitivity to subtle lesions, whose visual evidence is often sparse, low-contrast, and embedded within complex anatomical context. As local visual tokens are aggregated, these weak lesion cues can become underrepresented in global image representations, making them difficult for medical VLMs to recognize. Existing efforts to improve lesion sensitivity mainly rely on medical-domain vision-encoder pre-training, clinical-term-guided alignment, or trainable pathological representation enhancement. Although effective, these approaches usually require additional training or model-specific adaptation and may overfit to particular disease morphologies, limiting their applicability to frozen medical VLMs. To address these limitations, we propose EasyLens, a training-free plug-and-play subtle-lesion representation amplifier for medical VLMs. EasyLens first constructs EasyBank, a pathology-anatomy prototype space that provides lesion-related prototypes and anatomy-aware normal references for comparing suspicious patches against both pathological and normal anatomical patterns. To avoid blindly amplifying normal tissues, EasyTag selects lesion-relevant patches through counterfactual prototype reasoning. To counteract the dilution of subtle lesion cues in global image representations, EasyAmplifier strengthens the selected lesion-relevant patch representations through morphology-guided residual enhancement, thereby increasing their contribution to the global image embedding. Experiments on multiple medical image datasets and frozen medical VLM backbones show that EasyLens improves subtle-lesion detection and outperforms existing encoder-enhancement baselines.