arXiv 2203.021612022-03-04eess.IVcs.CVcs.LG

MF-Hovernet: An Extension of Hovernet for Colon Nuclei Identification and Counting (CoNiC) Challenge

Chonnam National University（全南国立大学）；Chonnam National University Medical School（全南国立大学医学院）

Vi Thi-Tuong Vo, Soo-Hyung Kim, Taebum Lee

英文摘要：

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.

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