Pre-training deep learning models with large data sets of natural images, such as ImageNet, has become the standard for endoscopic image analysis. This approach is generally superior to , due to the scarcity of high-quality medical imagery and labels. However, it is still unknown whether the learned features on natural imagery provide an optimal starting point for the downstream medical endoscopic imaging tasks. Intuitively, pre-training with imagery closer to the target domain could lead to better-suited feature representations. This study evaluates whether leveraging in-domain pre-training in gastrointestinal endoscopic image analysis has potential benefits compared to pre-training on natural images.

中文翻译：

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胃肠内窥镜人工智能的基础模型：架构、预训练方法和数据效率的影响


使用自然图像大数据集预训练深度学习模型，例如 ImageNet，已成为内窥镜图像分析的标准。由于缺乏高质量的医学图像和标签，这种方法通常优于 。然而，尚不清楚自然图像的学习特征是否为下游医学内窥镜成像任务提供最佳起点。直观地说，使用更接近目标域的图像进行预训练可能会产生更适合的特征表示。这项研究评估了与自然图像的预训练相比，在胃肠内窥镜图像分析中利用域内预训练是否具有潜在的好处。