论文标题
合成肿瘤使AI段肿瘤更好
Synthetic Tumors Make AI Segment Tumors Better
论文作者
论文摘要
我们制定了一种新的策略来产生合成肿瘤。与现有作品不同，我们策略产生的肿瘤具有两个有趣的优势：（1）形状和质地现实，即使是医疗专业人员也可能将其与实际肿瘤混淆。 （2）对AI模型训练有效，该模型训练可以进行肝肿瘤分割类似于对实际肿瘤训练的模型类似 - 这是前所未有的，因为迄今为止，没有现有的工作仅使用合成肿瘤，但与对实际肿瘤训练的模型相似甚至接近性能。该结果还意味着，在未来训练AI模型的训练中，可以大大减少对肿瘤进行人均注释的手动努力。此外，我们的合成肿瘤有可能通过自动产生小（或微小）合成肿瘤的巨大实例来提高小肿瘤检测的成功率。
We develop a novel strategy to generate synthetic tumors. Unlike existing works, the tumors generated by our strategy have two intriguing advantages: (1) realistic in shape and texture, which even medical professionals can confuse with real tumors; (2) effective for AI model training, which can perform liver tumor segmentation similarly to a model trained on real tumors - this result is unprecedented because no existing work, using synthetic tumors only, has thus far reached a similar or even close performance to the model trained on real tumors. This result also implies that manual efforts for developing per-voxel annotation of tumors (which took years to create) can be considerably reduced for training AI models in the future. Moreover, our synthetic tumors have the potential to improve the success rate of small tumor detection by automatically generating enormous examples of small (or tiny) synthetic tumors.
