论文标题
使用点注释的热图回归用于病变检测
Heatmap Regression for Lesion Detection using Pointwise Annotations
论文作者
论文摘要
在许多临床背景下，检测所有病变对于评估疾病活动至关重要。尽管获取分割标签的耗时性，但标准方法仍将病变检测作为分割问题。在本文中，我们提出了一种仅依赖点标签的病变检测方法。我们的模型通过热图回归训练，可以以概率方式检测可变数量的病变。实际上，我们提出的后处理方法提供了一种直接估计病变存在不确定性的可靠方法。 GAD病变检测的实验结果表明，与昂贵的分割标签的培训相比，我们的基于点的方法具有竞争力。最后，我们的检测模型为分割提供了合适的预训练。仅在17个细分样本上进行微调时，我们可以实现与完整数据集培训相当的性能。
In many clinical contexts, detecting all lesions is imperative for evaluating disease activity. Standard approaches pose lesion detection as a segmentation problem despite the time-consuming nature of acquiring segmentation labels. In this paper, we present a lesion detection method which relies only on point labels. Our model, which is trained via heatmap regression, can detect a variable number of lesions in a probabilistic manner. In fact, our proposed post-processing method offers a reliable way of directly estimating the lesion existence uncertainty. Experimental results on Gad lesion detection show our point-based method performs competitively compared to training on expensive segmentation labels. Finally, our detection model provides a suitable pre-training for segmentation. When fine-tuning on only 17 segmentation samples, we achieve comparable performance to training with the full dataset.
