论文标题
多任务深神经网络的核综合分割和组成回归
Nuclei panoptic segmentation and composition regression with multi-task deep neural networks
论文作者
论文摘要
血久和曙红染色的组织学图像中的核分割，分类和定量图像可以提取可解释的基于细胞的特征，这些特征可用于计算病理学中的下游可解释模型。举行了结肠核识别和计数（锥形）挑战，以帮助推动计算病理学中自动核识别的前进研究和创新。该报告描述了我们提出的锥体挑战的建议方法。我们的方法采用了多任务学习框架，该框架执行了全景分割任务和回归任务。对于全景分割任务，我们使用编码器型类型的深神经网络，除分割图外，还可以预测方向图，以将邻近的核分开为不同的实例
Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained histology images enables the extraction of interpretable cell-based features that can be used in downstream explainable models in computational pathology. The Colon Nuclei Identification and Counting (CoNIC) Challenge is held to help drive forward research and innovation for automatic nuclei recognition in computational pathology. This report describes our proposed method submitted to the CoNIC challenge. Our method employs a multi-task learning framework, which performs a panoptic segmentation task and a regression task. For the panoptic segmentation task, we use encoder-decoder type deep neural networks predicting a direction map in addition to a segmentation map in order to separate neighboring nuclei into different instances
