EasyLens: 一种无需训练的即插即用型微病变表示放大器，用于医学视觉语言模型
EasyLens: A Training-Free Plug-and-Play Subtle-Lesion Representation Amplifier for Medical Vision-Language Models
AI总结：
提出EasyLens，一种无需训练的即插即用模块，通过构建病理-解剖原型空间、反事实推理选择病变相关补丁以及形态引导残差增强，放大医学视觉语言模型对微病变的表示能力。
摘要：
医学视觉语言模型（VLM）在临床图像解读（包括病变检测和报告生成）方面显示出越来越大的潜力。然而，其对微病变的敏感性不足限制了其实用性，因为微病变的视觉证据通常稀疏、低对比度且嵌入复杂的解剖背景中。随着局部视觉标记的聚合，这些微弱的病变线索在全局图像表示中可能变得代表性不足，使得医学VLM难以识别。现有的提高病变敏感性的工作主要依赖于医学领域的视觉编码器预训练、临床术语引导的对齐或可训练的病理表示增强。尽管有效，但这些方法通常需要额外训练或模型特定适配，并可能过度适应特定疾病形态，限制了其在冻结的医学VLM上的适用性。为解决这些限制，我们提出EasyLens，一种无需训练的即插即用型微病变表示放大器，用于医学VLM。EasyLens首先构建EasyBank，一个病理-解剖原型空间，提供病变相关原型和解剖感知的正常参考，用于将可疑补丁与病理和正常解剖模式进行比较。为避免盲目放大正常组织，EasyTag通过反事实原型推理选择病变相关补丁。为抵消全局图像表示中微病变线索的稀释，EasyAmplifier通过形态引导的残差增强强化所选病变相关补丁的表示，从而增加其对全局图像嵌入的贡献。在多个医学图像数据集和冻结的医学VLM骨干上的实验表明，EasyLens改进了微病变检测，并优于现有的编码器增强基线。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
