论文标题
肾细胞癌检测和亚型，在全扫描图像中以最小的基于点的注释
Renal Cell Carcinoma Detection and Subtyping with Minimal Point-Based Annotation in Whole-Slide Images
论文作者
论文摘要
在医学成像中获得大量标记的数据是费力且耗时的，尤其是对于组织病理学而言。但是，从全扫描图像（WSIS）中获取未标记的数据要容易得多，便宜。半监督学习（SSL）是使用未标记数据并减轻标记数据的有效方法。因此，我们提出了一个使用SSL方法的框架，该框架用一种新颖的注释方法来准确检测癌区域，称为最小点的注释，然后利用创新的混合损失来训练分类模型以进行亚型。注释者只需要标记一些点，而在每个WSI中都标记为癌症。对肾细胞癌（RCC）的三种显着亚型的实验证明，经过最小注释数据集训练的分类器的性能与经过分割注释的数据集进行癌症区域检测的分类器相当。在测试WSI的F1得分方面，亚型模型的表现优于仅诊断标签训练的模型。
Obtaining a large amount of labeled data in medical imaging is laborious and time-consuming, especially for histopathology. However, it is much easier and cheaper to get unlabeled data from whole-slide images (WSIs). Semi-supervised learning (SSL) is an effective way to utilize unlabeled data and alleviate the need for labeled data. For this reason, we proposed a framework that employs an SSL method to accurately detect cancerous regions with a novel annotation method called Minimal Point-Based annotation, and then utilize the predicted results with an innovative hybrid loss to train a classification model for subtyping. The annotator only needs to mark a few points and label them are cancer or not in each WSI. Experiments on three significant subtypes of renal cell carcinoma (RCC) proved that the performance of the classifier trained with the Min-Point annotated dataset is comparable to a classifier trained with the segmentation annotated dataset for cancer region detection. And the subtyping model outperforms a model trained with only diagnostic labels by 12% in terms of f1-score for testing WSIs.
