论文标题
历史理学中的多任务学习，可广泛概括的模型
Multi-Task Learning in Histo-pathology for Widely Generalizable Model
论文作者
论文摘要
在这项工作中，我们显示了计算病理领域深度多任务学习的初步结果。我们结合了11项任务，包括斑块的口腔癌分类，这是发展中国家最普遍的癌症之一，到多组织核实例分割和分类。
In this work we show preliminary results of deep multi-task learning in the area of computational pathology. We combine 11 tasks ranging from patch-wise oral cancer classification, one of the most prevalent cancers in the developing world, to multi-tissue nuclei instance segmentation and classification.
