Work

Cross-Scale Guidance Integration Transformer for Instance Segmentation in Pathology Images

Year: 2025

Type: article

Abstract: Goal:To assess the degree of adenocarcinoma, pathologists need to manually review pathology images. To reduce their burdens and achieve good inter-observer as well as intra-observer reproducibility, instance segmentation methods can help pathologists quantify shapes of gland cells and provide an automatic solution for computer-assisted grading of adenocarcinoma. However, segmenting individual gland cells of different sizes remains a difficult challenge in computer aided diagnosis.Method:A novel cross-scale guidance integration transformer is proposed for gland cell instance segmentation. Our network contains a cross-scale guidance integration module to integrate multi-scale features learned from the pathology image. By using the integrated features from different field-of-views, the decoder with mask attention can better segment individual gland cells.Results:Compared with recent task-specific deep learning methods, our method can achieve state-of-the-art performance in two public gland cell datasets.Conclusions:By imposing cross-scale encoder information, our method can retrieve accurate gland cell segmentation to assist the pathologists for computer-assisted grading of adenocarcinoma.

Source: IEEE Open Journal of Engineering in Medicine and Biology

DOI: 10.1109/ojemb.2025.3555818

Authors: Yung-Ming Kuo, Jia-Chun Sheng, Chen-Hsuan Lo, You-Jie Wu, Chun-Rong Huang

Institutions: National Formosa University, National Chung Hsing University, National Yang Ming Chiao Tung University

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,075