用于咬翼X线片龋齿分类的可解释、交互式深度多示例学习
Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-rays
AI总结：
提出一种可解释、可交互的深度多示例学习架构，用于咬翼X线片龋齿分类，可在弱标签下输出局部热力图并接受分割指导，在约3.8万张咬翼片上取得竞争力且分割指导显著提升分类与定位。
摘要：
我们提出一种简单高效的基于深度多示例学习的图像分类架构，并将其应用于牙科X光片中颇具挑战性的龋齿检测任务。在技术层面，我们的方法有两点贡献：首先，尽管仅使用弱图像级标签进行训练，它仍能输出局部图像块分类概率的热力图。其次，它能够利用分割标签进行学习，以指导训练。与现有方法不同，人类用户可以忠实地解释预测结果，并与模型交互以决定应关注哪些区域。实验在一个包含约3.8万张咬翼片（约31.6万颗牙齿）的大型临床数据集上进行；与多种基线方法相比，我们取得了具有竞争力的性能。当由外部龋齿分割模型提供指导时，可观察到分类和定位性能的显著提升。
英文摘要：
We propose a simple and efficient image classification architecture based on deep multiple instance learning, and apply it to the challenging task of caries detection in dental radiographs. Technically, our approach contributes in two ways: First, it outputs a heatmap of local patch classification probabilities despite being trained with weak image-level labels. Second, it is amenable to learning from segmentation labels to guide training. In contrast to existing methods, the human user can faithfully interpret predictions and interact with the model to decide which regions to attend to. Experiments are conducted on a large clinical dataset of $\sim$38k bitewings ($\sim$316k teeth), where we achieve competitive performance compared to various baselines. When guided by an external caries segmentation model, a significant improvement in classification and localization performance is observed.
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。
