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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Veröffentlicht in:	Biomedical signal processing and control Jg. 113; S. 108699
Hauptverfasser:	Chu, Hongbo, Chang, Zhengsong, Shao, Zihan, Deng, Pengyun, Wang, Yan, Guo, Xiaojing, Wang, Yang, Yang, Jiao, Guan, Tian, Li, Xiaomei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.03.2026
Schlagworte:	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-Zugang:	Volltext
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