BLoss-DDNet: bending loss and dual-task decoding network for overlapping cell nucleus segmentation of cervical clinical LBC images

20251 citationsJournal Articlegold Open Access

Authors

Guihua Yang · Daqing Normal University

Ziran Chen · Construction Development (Qatar)

Peng Guo · Construction Development (Qatar)

Junchi Ma · Daqing Normal University

Jinjie Huang · Harbin University of Science and Technology

Cong Jin · Xi'an Aeronautical University

Xiaona Yang

Abstract

Therefore, the proposed Bloss-DDNet can effectively address the segmentation problem of overlapping cell clusters and nuclei in clinical LBC images, providing strong support for subsequent clinical auxiliary diagnosis of cervical cancer.

Topics & Keywords

AI in cancer detection Advanced Neural Network Applications Cell Image Analysis Techniques

Publication Details

Published in: Frontiers in Artificial Intelligence

Volume 8, pp. 1649452-1649452

DOI: 10.3389/frai.2025.1649452

Field-Weighted Citation Impact: 2.83

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BLoss-DDNet: bending loss and dual-task decoding network for overlapping cell nucleus segmentation of cervical clinical LBC images

Authors

Abstract

Topics & Keywords

Publication Details