基于Transformer的全肺分析模型，用CT片预测肺癌风险。
LungEvaty: A Scalable, Open-Source Transformer-based Deep Learning Model for Lung Cancer Risk Prediction in LDCT Screening
- 用整个肺部影像输入，不依赖局部标注，实现高效建模
- 在超9万张扫描数据上训练，1-6年风险预测达顶尖水平
- 开源免费，适合做长期与多模态肺癌研究的基线工具
随着低剂量CT（LDCT）筛查在各国推广，肺癌风险评估日益重要。面对日益增长的影像数据量，高效处理完整肺部影像的方法至关重要。现有方法或过度依赖像素级标注，难以扩展；或分割肺部分析，影响性能。我们提出LungEvaty，一种基于Transformer的全肺深度学习框架，仅凭单次LDCT扫描即可预测1-6年肺癌风险。该模型直接从大规模筛查数据中学习，捕捉与恶性肿瘤相关的整体解剖和病理特征。无需区域监督，表现已达当前最优；可选的解剖引导注意力损失（AIAG）进一步增强关注关键区域的能力。模型在超过90,000张CT扫描上训练，其中28,000用于微调，6,000用于评估。LungEvaty提供简单、数据高效且完全开源的解决方案，为未来纵向与多模态肺癌风险预测研究奠定可扩展基础。
原文摘要 · Abstract (English)
Lung cancer risk estimation is gaining increasing importance as more countries introduce population-wide screening programs using low-dose CT (LDCT). As imaging volumes grow, scalable methods that can process entire lung volumes efficiently are essential to tap into the full potential of these large screening datasets. Existing approaches either over-rely on pixel-level annotations, limiting scalability, or analyze the lung in fragments, weakening performance. We present LungEvaty, a fully transformer-based framework for predicting 1-6 year lung cancer risk from a single LDCT scan. The model operates on whole-lung inputs, learning directly from large-scale screening data to capture comprehensive anatomical and pathological cues relevant for malignancy risk. Using only imaging data and no region supervision, LungEvaty matches state-of-the-art performance, refinable by an optional Anatomically Informed Attention Guidance (AIAG) loss that encourages anatomically focused attention. In total, LungEvaty was trained on more than 90,000 CT scans, including over 28,000 for fine-tuning and 6,000 for evaluation. The framework offers a simple, data-efficient, and fully open-source solution that provides an extensible foundation for future research in longitudinal and multimodal lung cancer risk prediction.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。