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:

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