Lung-CADex：胸CT图像中肺结节的全自动零样本检测与分类
Lung-CADex: Fully automatic Zero-Shot Detection and Classification of Lung Nodules in Thoracic CT Images
AI总结：
本文提出Lung-CADex框架，利用MedSAM和CLIP实现肺结节零样本检测（CADe），并通过放射组学特征对比学习实现良恶性分类（CADx），在LIDC和LUNGx数据集上灵敏度达0.86，优于全监督方法。
摘要：
肺癌数十年来一直是人类生命的主要威胁之一。计算机辅助诊断有助于早期肺结节检测，并促进后续的结节特征分析。大型视觉语言模型（VLMs）已被证明在多个依赖成像和文本数据的下游医学任务中有效。然而，利用VLMs进行病灶级检测及后续诊断尚未被探索。我们提出CADe，以零样本方式使用Segment Anything Model的一种变体MedSAM对肺结节进行分割。CADe通过前缀微调（prefix tuning）使用CLIP文本编码器，在输入计算机断层扫描（CT）上对提示套件进行训练。我们还提出CADx，一种通过构建放射组学特征库并通过对比学习对齐图像-特征对来进行结节良恶性特征分析的方法。CADe和CADx的训练与验证使用了最大的公开可用数据集之一LIDC。为检验模型的泛化能力，还在具有挑战性的数据集LUNGx上进行了评估。我们的实验结果表明，所提方法达到了0.86的灵敏度，而其他全监督方法的灵敏度为0.76。源代码、数据集和预处理数据可通过以下链接获取：
英文摘要：
Lung cancer has been one of the major threats to human life for decades. Computer-aided diagnosis can help with early lung nodul detection and facilitate subsequent nodule characterization. Large Visual Language models (VLMs) have been found effective for multiple downstream medical tasks that rely on both imaging and text data. However, lesion level detection and subsequent diagnosis using VLMs have not been explored yet. We propose CADe, for segmenting lung nodules in a zero-shot manner using a variant of the Segment Anything Model called MedSAM. CADe trains on a prompt suite on input computed tomography (CT) scans by using the CLIP text encoder through prefix tuning. We also propose, CADx, a method for the nodule characterization as benign/malignant by making a gallery of radiomic features and aligning image-feature pairs through contrastive learning. Training and validation of CADe and CADx have been done using one of the largest publicly available datasets, called LIDC. To check the generalization ability of the model, it is also evaluated on a challenging dataset, LUNGx. Our experimental results show that the proposed methods achieve a sensitivity of 0.86 compared to 0.76 that of other fully supervised methods.The source code, datasets and pre-processed data can be accessed using the link:
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。
