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...

Celý popis

Podrobná bibliografie

Vydáno v:	Biomedical signal processing and control Ročník 113; s. 108699
Hlavní autoři:	Chu, Hongbo, Chang, Zhengsong, Shao, Zihan, Deng, Pengyun, Wang, Yan, Guo, Xiaojing, Wang, Yang, Yang, Jiao, Guan, Tian, Li, Xiaomei
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.03.2026
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!