MF-Hovernet: An Extension of Hovernet for Colon Nuclei Identification and Counting (CoNiC) Challenge
This work addresses a domain-specific problem in medical imaging for colon cancer diagnosis, but it is incremental as it builds on an existing method.
The authors tackled nuclei identification and counting in colon cancer by extending Hovernet with multiple filter blocks, resulting in improved performance over the original model.
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.