HistoNeXt: dual-mechanism feature pyramid network for cell nuclear segmentation and classification.

Journal: BMC medical imaging
PMID: 39773093

Abstract

PURPOSE: To develop an end-to-end convolutional neural network model for analyzing hematoxylin and eosin(H&E)-stained histological images, enhancing the performance and efficiency of nuclear segmentation and classification within the digital pathology workflow.

Authors

  • Junxiao Chen
    Department of Information, Third Affiliated Hospital of Naval Medical University, No. 225 Changhai Road, Yangpu District, 200438, Shanghai, China.
  • Ruixue Wang
    State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China; School of National Security and Emergency Management, Beijing Normal University, Beijing, China.
  • Wei Dong
    Department of Cardiology, Chinese PLA General Hospital, Beijing, China.
  • Hua He
    State Key Laboratory of Heavy Oil Processing and College of Chemistry and Chemical Engineering, China University of Petroleum (East China), Qingdao 266580, China.
  • Shiyong Wang
    Neurosurgery of The First Affiliated Hospital, Jinan University, Guangzhou, China. Washiyong@126.com.

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