A4 Artikkeli konferenssijulkaisussa
Self-supervised Contrastive Pre-training Integrated with Multi-level Co-attention for Survival Prognosis from Whole Slide Images (2024)
Gao, J., Hao, X., Jin, S., & Xu, H. (2024). Self-supervised Contrastive Pre-training Integrated with Multi-level Co-attention for Survival Prognosis from Whole Slide Images. In P. You, S. Liu, & J. Wang (Eds.), Proceedings of International Conference on Image, Vision and Intelligent Systems 2023 (ICIVIS 2023) (pp. 650-658). Springer. Lecture Notes in Electrical Engineering, 1163. https://doi.org/10.1007/978-981-97-0855-0_62
JYU-tekijät tai -toimittajat
Julkaisun tiedot
Julkaisun kaikki tekijät tai toimittajat: Gao, Junxiu; Hao, Xinyu; Jin, Shan; Xu, Hongming
Emojulkaisu: Proceedings of International Conference on Image, Vision and Intelligent Systems 2023 (ICIVIS 2023)
Emojulkaisun toimittajat: You, Peng; Liu, Shuaiqi; Wang, Jun
Konferenssi:
- International Conference on Image, Vision and Intelligent Systems
Konferenssin paikka ja aika: Baoding, China, 16.-18.8.2023
ISBN: 978-981-97-0854-3
eISBN: 978-981-97-0855-0
Lehti tai sarja: Lecture Notes in Electrical Engineering
ISSN: 1876-1100
eISSN: 1876-1119
Julkaisuvuosi: 2024
Ilmestymispäivä: 25.02.2024
Sarjan numero: 1163
Artikkelin sivunumerot: 650-658
Kirjan kokonaissivumäärä: 770
Kustantaja: Springer
Julkaisumaa: Singapore
Julkaisun kieli: englanti
DOI: https://doi.org/10.1007/978-981-97-0855-0_62
Julkaisun avoin saatavuus: Ei avoin
Julkaisukanavan avoin saatavuus:
Tiivistelmä
Survival analysis is of paramount importance in guiding the development of optimal treatment strategies for cancer patients. Because of the rich prognostic information contained in whole slide images (WSIs), multiple instance learning (MIL) approaches integrated with WSI analysis have been widely used in survival risk prediction. However, existing MIL methods often fail to encompass the complete range of histological image features, including critical information from local tissue regions, which limits their performance in survival prognosis. To address this limitation, we employ a self-supervised learning mechanism to train a feature extractor which can capture the intricate characteristics of WSIs. Furthermore, we introduce an attention mechanism that incorporates local patches and clusters to guide the fusion of multi-level features for survival outcome prediction. The proposed method demonstrates excellent performance on the widely recognized TCGA-COAD dataset. Experimental findings indicate that the integration of pre-trained feature extractors with MIL method and the fusion of multi-level histological features yield notable advancements in survival risk predictions.
YSO-asiasanat: syöpätaudit; ennusteet; histologia; konenäkö; koneoppiminen
Liittyvät organisaatiot
OKM-raportointi: Kyllä
Raportointivuosi: 2024
Alustava JUFO-taso: 1