论文标题
基于染色的对比度训练用于组织病理学图像分析
Stain Based Contrastive Co-training for Histopathological Image Analysis
论文作者
论文摘要
我们提出了一种新型的半监督学习方法，用于分类组织病理学图像。我们采用贴片级注释的强大监督，并结合新的共同训练损失，创建一个半监督的学习框架。共培训依赖于多种有条件独立且充分的数据观点。我们使用颜色反卷积在病理图像中分离苏木精和曙红通道，从而创建每个幻灯片的两个视图，这些视图可以部分满足这些要求。两个单独的CNN用于将两个视图嵌入关节特征空间中。我们在此特征空间中使用对比的损失来实施共同训练。我们在清晰的细胞肾细胞和前列腺癌中评估了我们的方法，并证明了对最先进的半监督学习方法的改善。
We propose a novel semi-supervised learning approach for classification of histopathology images. We employ strong supervision with patch-level annotations combined with a novel co-training loss to create a semi-supervised learning framework. Co-training relies on multiple conditionally independent and sufficient views of the data. We separate the hematoxylin and eosin channels in pathology images using color deconvolution to create two views of each slide that can partially fulfill these requirements. Two separate CNNs are used to embed the two views into a joint feature space. We use a contrastive loss between the views in this feature space to implement co-training. We evaluate our approach in clear cell renal cell and prostate carcinomas, and demonstrate improvement over state-of-the-art semi-supervised learning methods.
