Generalizability is a concern when applying a deep learning (DL) model trained on one dataset to other datasets. It is challenging to demonstrate a DL model’s generalizability efficiently and sufficiently before implementing the model in clinical practice. Training a universal model that works anywhere, anytime, for anybody is unrealistic. In this work, we demonstrate the generalizability problem, then explore potential solutions based on transfer learning by using the cone-beam computed tomography (CBCT) to computed tomography (CT) image conversion task as the testbed. Previous works only studied on one or two anatomical sites and used images from the same vendor’s scanners. Here, we investigated how a model trained for one machine and one anatomical site works on other machines and other anatomical sites. We trained a model on CBCT images acquired from one vendor’s scanners for head and neck cancer patients and applied it to images from another vendor’s scanners and for prostate, pancreatic, and cervical cancer patients. We found that generalizability could be a significant problem for this particular application when applying a trained DL model to datasets from another vendor’s scanners. We then explored three practical solutions based on transfer learning to solve this generalization problem: the target model, which is trained on a target dataset from scratch; the combined model, which is trained on both source and target datasets from scratch; and the adapted model, which fine-tunes the trained source model to a target dataset. We found that when there are sufficient data in the target dataset, all three models can achieve good performance. When the target dataset is limited, the adapted model works the best, which indicates that using the fine-tuning strategy to adapt the trained model to an unseen target dataset is a viable and easy way to implement DL models in the clinic.

将在一个数据集上训练的深度学习（DL）模型应用于其他数据集时，泛化性是一个问题。在临床实践中充分有效地证明DL模型的可推广性具有挑战性。培训一个在任何地方，任何时间，任何人都可以使用的通用模型是不现实的。在这项工作中，我们演示了可推广性问题，然后通过使用锥束计算机断层摄影（CBCT）到计算机断层摄影（CT）图像转换任务作为测试平台，探索了基于转移学习的潜在解决方案。以前的作品仅在一个或两个解剖部位上进行研究，并使用了来自同一供应商的扫描仪的图像。在这里，我们研究了为一台机器和一个解剖部位训练的模型在其他机器和其他解剖部位上如何工作。我们针对从一个供应商的扫描仪获取的头颈癌患者的CBCT图像训练了模型，并将其应用于另一供应商的扫描仪以及前列腺癌，胰腺癌和宫颈癌患者的图像。我们发现，将训练有素的DL模型应用于来自其他供应商的扫描仪的数据集时，对于该特定应用程序，泛化性可能是一个重大问题。然后，我们基于迁移学习探索了三种实用的解决方案来解决此泛化问题：目标模型，该模型从头开始在目标数据集上进行训练；组合模型，从头开始对源和目标数据集进行训练；以及调整后的模型，该模型将训练后的源模型微调到目标数据集。我们发现，当目标数据集中有足够的数据时，这三个模型都可以达到良好的性能。