The RADLER framework is demonstrated with a DL ar- chitecture for CT/PET; in detail, a 3D multimodal CNN is adapted from a 2D solution originally aimed at classifying lung nodules from CT imaging [39]. Secondly, we adopted an internal transfer learning approach, starting from the diag- nostic classification of tumour stage. This domain adaptation approach proved useful in dealing with class unbalance and a relatively low number of samples, while achieving good predictive performance, as shown on the HN dataset, with high class unbalance and low number of samples. 解释

这段话大概是在说明RADLER框架采用了一个针对CT/PET的DL架构；具体而言，是从最初旨在从CT成像中对肺结节进行分类的2D解决方案中改编的3D多模态CNN。其次，我们采用了一种内部迁移学习方法，从肿瘤分期的诊断分类开始。这种领域自适应方法在处理类别不平衡和相对较少的样本时证明是有用的，同时在HN数据集上取得了良好的预测性能，该数据集具有高度类别不平衡和少量样本。