Enhancing poorly differentiated lung cancer classification with rotary position embedding and sparse attention in multiple instance learning

In clinical practice, diagnosing poorly differentiated non-small cell lung cancer (NSCLC) typically requires immunohistochemistry (IHC) to accurately distinguish between cancer subtypes. The high cost and time-consuming nature of this process significantly limit its clinical applicability. Furthermo...

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Bibliographic Details
Published in:Biomedical signal processing and control Vol. 113; p. 108699
Main Authors: Chu, Hongbo, Chang, Zhengsong, Shao, Zihan, Deng, Pengyun, Wang, Yan, Guo, Xiaojing, Wang, Yang, Yang, Jiao, Guan, Tian, Li, Xiaomei
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.03.2026
Subjects:
Deep learning
Histopathological classification
Multiple instance learning
Poorly differentiated lung cancer
Transformer
Deep learning
Transformer
Histopathological classification
Poorly differentiated lung cancer
Multiple instance learning
ISSN:1746-8094
Online Access:Get full text
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Summary:In clinical practice, diagnosing poorly differentiated non-small cell lung cancer (NSCLC) typically requires immunohistochemistry (IHC) to accurately distinguish between cancer subtypes. The high cost and time-consuming nature of this process significantly limit its clinical applicability. Furthermore, the limited availability of tissue samples from advanced-stage patients poses a significant challenge for subsequent diagnosis. Consequently, leveraging novel AI technologies is critical to overcoming this challenge. Hence, we propose an innovative Multi-Instance Learning (MIL) algorithm, RoSA-MIL. This algorithm is based on a novel MIL framework that integrates relative position encoding and sparse attention mechanisms. Relative position encoding enhances the model’s comprehension of spatial relationships between samples, thereby improving its ability to capture local context. The sparse attention mechanism reduces computational overhead by eliminating redundant calculations, thereby enhancing both efficiency and scalability. Experiments conducted on diverse datasets from three independent centers demonstrate that the RoSA-MIL model outperforms existing methods, exhibiting significant improvements in diagnostic accuracy, robustness, and efficiency when processing large-scale data.
ISSN:1746-8094
DOI:10.1016/j.bspc.2025.108699