论文标题
Hydramix-NET：一种深层多任务半监督学习方法，用于细胞检测和分类
HydraMix-Net: A Deep Multi-task Semi-supervised Learning Approach for Cell Detection and Classification
论文作者
论文摘要
半监督技术通过利用未标记的数据来改善模型的性能来消除标记设置的大规模标记的障碍。在本文中，我们提出了一个半监督的深层多任务分类和本地化方法在医学想象领域的Hydramix-net，在该领域的标签耗时且昂贵。首先，使用模型对带有平均图像的增强图像集的预测来生成伪标签。高熵预测将进一步锐化以减少熵，然后将其与标记的训练集混合。该模型以多任务学习方式训练，与简单的深层模型相比，当数据有限时，具有噪声耐受性关节损失，并在有限的数据中实现更好的性能。在DLBCL数据上，仅给出100个标记的示例时，与简单的CNN达到70 \％精度相比，它达到80 \％的准确性。
Semi-supervised techniques have removed the barriers of large scale labelled set by exploiting unlabelled data to improve the performance of a model. In this paper, we propose a semi-supervised deep multi-task classification and localization approach HydraMix-Net in the field of medical imagining where labelling is time consuming and costly. Firstly, the pseudo labels are generated using the model's prediction on the augmented set of unlabelled image with averaging. The high entropy predictions are further sharpened to reduced the entropy and are then mixed with the labelled set for training. The model is trained in multi-task learning manner with noise tolerant joint loss for classification localization and achieves better performance when given limited data in contrast to a simple deep model. On DLBCL data it achieves 80\% accuracy in contrast to simple CNN achieving 70\% accuracy when given only 100 labelled examples.
