Nuclei segmentation and classification is the first and most crucial step
that is utilized for many different microscopy medical analysis applications.
However, it suffers from many issues such as the segmentation of small objects,
imbalance, and fine-grained differences between types of nuclei. In this paper,
multiple different contributions were done tackling these problems present.
Firstly, the recently released "ConvNeXt" was used as the encoder for HoVer-Net
model since it leverages the key components of transformers that make them
perform well. Secondly, to enhance the visual differences between nuclei, a
multi-channel color space-based approach is used to aid the model in extracting
distinguishing features. Thirdly, Unified Focal loss (UFL) was used to tackle
the background-foreground imbalance. Finally, Sharpness-Aware Minimization
(SAM) was used to ensure generalizability of the model. Overall, we were able
to outperform the current state-of-the-art (SOTA), HoVer-Net, on the
preliminary test set of the CoNiC Challenge 2022 by 12.489% mPQ+.

本研究提出了一种基于多通道颜色空间和统一聚焦损失函数（UFL）的核分割和分类方法，同时使用 ConvNeXt 作为 HoVer-Net 模型的编码器，使其能够自动提取区分特征，并使用 SAM 算法确保模型的普适性和达到了 12.489% mPQ + 的测试效果，超越当前 SOTA，适用于多种医学分析应用。