论文标题
MF-HoverNet：用于结肠核鉴定和计数（锥）挑战的气垫网的扩展
MF-Hovernet: An Extension of Hovernet for Colon Nuclei Identification and Counting (CoNiC) Challenge
论文作者
论文摘要
核鉴定和计数是癌症的最重要的形态特征，尤其是在结肠中。已经提出了许多基于深度学习的方法来解决这个问题。在这项工作中，我们构建了用于核识别和计数的Hovernet的扩展，以解决名为MF-HoverNet的问题。我们提出的模型是将多个档案块与气垫网构建结构的组合。当前的结果显示了多个滤波器块的效率，以提高原始气垫网模型的性能。
Nuclei Identification and Counting is the most important morphological feature of cancers, especially in the colon. Many deep learning-based methods have been proposed to deal with this problem. In this work, we construct an extension of Hovernet for nuclei identification and counting to address the problem named MF-Hovernet. Our proposed model is the combination of multiple filer block to Hovernet architecture. The current result shows the efficiency of multiple filter block to improve the performance of the original Hovernet model.
