Nuclear shape and orientation features from H&E images predict survival in early-stage estrogen receptor-positive breast cancers. The authors declare no conflict of interest. Future work will involve improving the class balance of data in simultaneous learning. We observe low classification scores for nuclei with fewer samples and high variability. Our nucleus segmentation and classification model allows for the identification of morphological characteristics and quantification of the different types of nuclei and, thus, can provide additional diagnostic and predictive value. It not only facilitates an excellent opportunity to speed up the diagnostic process in the clinic but also increases our understanding of tissue characteristics, leading to improved patient care and management. The ability to segment and classify nuclei of different types automatically is directly associated with subsequent pathological analysis. The experimental results show the effectiveness of the multiple filter unit in improving the performance of the original HoVer-Net model as well as outperforming other models. Our method integrates multiple filter units into HoVer-Net with attention gates. This paper presented a method for nucleus segmentation and classification from pathology images. The ability to segment and classify different types of nuclei automatically has a direct influence on further pathological analysis, offering great potential not only to accelerate the diagnostic process in clinics but also for enhancing our understanding of tissue and cell properties to improve patient care and management. In addition, our experimental results show that the Mulvernet achieves outperforming results in both nuclei segmentation and classification compared to several methods. The results of the study will significantly impact cell segmentation and classification by showing that a multiple filter unit improves the performance of the original HoVer-Net model. This study aims to develop a new method to address these problems based primarily on the horizontal and vertical distance network (HoVer-Net), multiple filter units, and attention gate mechanisms. Common problems in these tasks arise from the inconsistent sizes and shapes of the cells in each pathology image. Automated nuclear classification and segmentation methods support analysis and understanding of cell characteristics and functions, and allow the analysis of large-scale nuclear forms in the diagnosis and treatment of diseases. Nucleus segmentation and classification are crucial in pathology image analysis.
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