视频预训练推进了胸部CT任务上的3D深度学习
Video Pretraining Advances 3D Deep Learning on Chest CT Tasks
AI总结：
本研究探讨自然视频预训练在3D医学任务中的应用，发现其能显著提升七种3D模型在两个胸部CT数据集上的表现，并超越2D基线，支持从小规模域内预训练转向大规模域外预训练。
摘要：
在ImageNet等大型自然图像分类数据集上进行预训练，有助于在数据稀缺的2D医学任务上开发模型。3D医学任务的数据量通常远少于2D医学任务，促使从业者依赖预训练的2D模型来对切片进行特征化。然而，由于这些2D模型无法原生利用横截面或时间信息，它们在3D计算机视觉基准上已被3D模型超越。在本研究中，我们探讨了针对3D模型的自然视频预训练能否在3D医学任务的小型数据集上实现更高性能。我们证明，无论微调数据集大小如何，视频预训练均能提升七种3D模型在两个胸部CT数据集上的平均表现，并且视频预训练使3D模型的性能超越了2D基线。最后，我们观察到，在大规模域外Kinetics数据集上进行预训练，比在典型规模的域内CT数据集上预训练能带来更大的性能提升。我们的结果表明，视频预训练在广泛的架构、任务和训练数据集规模上均具有一致的益处，支持在3D医学任务中从小规模域内预训练转向大规模域外预训练。
英文摘要：
Pretraining on large natural image classification datasets such as ImageNet has aided model development on data-scarce 2D medical tasks. 3D medical tasks often have much less data than 2D medical tasks, prompting practitioners to rely on pretrained 2D models to featurize slices. However, these 2D models have been surpassed by 3D models on 3D computer vision benchmarks since they do not natively leverage cross-sectional or temporal information. In this study, we explore whether natural video pretraining for 3D models can enable higher performance on smaller datasets for 3D medical tasks. We demonstrate video pretraining improves the average performance of seven 3D models on two chest CT datasets, regardless of finetuning dataset size, and that video pretraining allows 3D models to outperform 2D baselines. Lastly, we observe that pretraining on the large-scale out-of-domain Kinetics dataset improves performance more than pretraining on a typically-sized in-domain CT dataset. Our results show consistent benefits of video pretraining across a wide array of architectures, tasks, and training dataset sizes, supporting a shift from small-scale in-domain pretraining to large-scale out-of-domain pretraining for 3D medical tasks. Our code is available at: https://github.com/rajpurkarlab/chest-ct-pretraining
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。
