论文标题
通过自然图像的涂鸦监督在数字病理学中使用较少的标签学习
Learning with Less Labels in Digital Pathology via Scribble Supervision from Natural Images
论文作者
论文摘要
培训数字病理（DP）领域深度学习模型的关键挑战是医学专家的高注释成本。解决此问题的一种方法是通过从自然图像域（NI）进行转移学习，在该域（NI）中，注释成本便宜得多。从NI到DP的跨域转移学习通过班级标签表明是成功的。依靠类标签的一个潜在弱点是缺乏空间信息，可以从空间标签（例如全像素的分段标签和涂鸦标签）中获得。我们证明，NI域中的涂鸦标签可以提高在两个癌症分类数据集（Patch Camelyon乳腺癌和大肠癌数据集）上DP模型的性能。此外，我们表明，经过涂鸦标签训练的模型尽管要容易得多，而且收集速度更快，而且收集更快。
A critical challenge of training deep learning models in the Digital Pathology (DP) domain is the high annotation cost by medical experts. One way to tackle this issue is via transfer learning from the natural image domain (NI), where the annotation cost is considerably cheaper. Cross-domain transfer learning from NI to DP is shown to be successful via class labels. One potential weakness of relying on class labels is the lack of spatial information, which can be obtained from spatial labels such as full pixel-wise segmentation labels and scribble labels. We demonstrate that scribble labels from NI domain can boost the performance of DP models on two cancer classification datasets (Patch Camelyon Breast Cancer and Colorectal Cancer dataset). Furthermore, we show that models trained with scribble labels yield the same performance boost as full pixel-wise segmentation labels despite being significantly easier and faster to collect.
